diff --git a/README.md b/README.md index 28ccf3c7..7c23cce3 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,23 @@ # Human Library -### 3D Face Detection, Face Embedding & Recognition, -### Body Pose Tracking, Hand & Finger Tracking, -### Iris Analysis, Age & Gender & Emotion Prediction -### & Gesture Recognition +**3D Face Detection, Face Embedding & Recognition,** +**Body Pose Tracking, Hand & Finger Tracking,** +**Iris Analysis, Age & Gender & Emotion Prediction** +**& Gesture Recognition**
Native JavaScript module using TensorFlow/JS Machine Learning library -Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux +Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux + - Browser/WebWorker: Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends - NodeJS: Compatible with software *tfjs-node* and CUDA accelerated backends *tfjs-node-gpu* +Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) for processing of live WebCam video or static images +
-### Project pages: +## Project pages - [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) - [**Code Repository**](https://github.com/vladmandic/human) @@ -24,7 +27,7 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
-### Wiki pages: +## Wiki pages - [**Home**](https://github.com/vladmandic/human/wiki) - [**Demos**](https://github.com/vladmandic/human/wiki/Demos) @@ -37,7 +40,7 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
-### Additional notes: +## Additional notes - [**Notes on Backends**](https://github.com/vladmandic/human/wiki/Backends) - [**Development Server**](https://github.com/vladmandic/human/wiki/Development-Server) @@ -49,13 +52,35 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
-*See [issues](https://github.com/vladmandic/human/issues?q=) and [discussions](https://github.com/vladmandic/human/discussions) for list of known limitations and planned enhancements* +## Default models + +Default models in Human library are: + +- **Face Detection**: MediaPipe BlazeFace-Back +- **Face Mesh**: MediaPipe FaceMesh +- **Face Iris Analysis**: MediaPipe Iris +- **Emotion Detection**: Oarriaga Emotion +- **Gender Detection**: Oarriaga Gender +- **Age Detection**: SSR-Net Age IMDB +- **Body Analysis**: PoseNet +- **Face Embedding**: Sirius-AI MobileFaceNet Embedding + +Note that alternative models are provided and can be enabled via configuration +For example, `PoseNet` model can be switched for `BlazePose` model depending on the use case + +For more info, see [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration) and [**List of Models**](https://github.com/vladmandic/human/wiki/Models) + +
+ +*See [**issues**](https://github.com/vladmandic/human/issues?q=) and [**discussions**](https://github.com/vladmandic/human/discussions) for list of known limitations and planned enhancements* *Suggestions are welcome!*

-## Options ## +## Options + +As presented in the demo application... ![Options visible in demo](assets/screenshot-menu.png) @@ -65,12 +90,17 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
+**Training image:** + +![Example Training Image](assets/screenshot-sample.png) + **Using static images:** -![Example Using Image](assets/screenshot1.jpg) +![Example Using Image](assets/screenshot-images.jpg) + +**Live WebCam view:** + +![Example Using WebCam](assets/screenshot-webcam.jpg) +
- -**Using webcam:** - -![Example Using WebCam](assets/screenshot2.jpg) diff --git a/TODO.md b/TODO.md index 0fd667cc..7197fbe0 100644 --- a/TODO.md +++ b/TODO.md @@ -1,7 +1,5 @@ # To-Do list for Human library -- Explore BlazePose model -- Explore MediaPipe TFLite models - Fix BlazeFace NodeJS missing ops - Prune pre-packaged models - Build Face embedding database diff --git a/assets/screenshot-images.jpg b/assets/screenshot-images.jpg new file mode 100644 index 00000000..3bf8a8c6 Binary files /dev/null and b/assets/screenshot-images.jpg differ diff --git a/assets/screenshot-menu.png b/assets/screenshot-menu.png index 495c7521..53b03f22 100644 Binary files a/assets/screenshot-menu.png and b/assets/screenshot-menu.png differ diff --git a/assets/screenshot-sample.png b/assets/screenshot-sample.png new file mode 100644 index 00000000..3627c293 Binary files /dev/null and b/assets/screenshot-sample.png differ diff --git a/assets/screenshot-webcam.jpg b/assets/screenshot-webcam.jpg new file mode 100644 index 00000000..ed25d701 Binary files /dev/null and b/assets/screenshot-webcam.jpg differ diff --git a/assets/screenshot1.jpg b/assets/screenshot1.jpg deleted file mode 100644 index 0e958474..00000000 Binary files a/assets/screenshot1.jpg and /dev/null differ diff --git a/assets/screenshot2.jpg b/assets/screenshot2.jpg deleted file mode 100644 index 5103f3e7..00000000 Binary files a/assets/screenshot2.jpg and /dev/null differ diff --git a/config.js b/config.js index 8d8be31e..dd44ff2d 100644 --- a/config.js +++ b/config.js @@ -105,8 +105,7 @@ export default { age: { enabled: true, - modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki' - // which determines training set for model + modelPath: '../models/age-ssrnet-imdb.json', inputSize: 64, // fixed value skipFrames: 31, // how many frames to go without re-running the detector // only used for video inputs @@ -115,7 +114,7 @@ export default { gender: { enabled: true, minConfidence: 0.1, // threshold for discarding a prediction - modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki' + modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb' inputSize: 64, // fixed value skipFrames: 41, // how many frames to go without re-running the detector // only used for video inputs @@ -126,7 +125,7 @@ export default { inputSize: 64, // fixed value minConfidence: 0.1, // threshold for discarding a prediction skipFrames: 21, // how many frames to go without re-running the detector - modelPath: '../models/emotion-large.json', // can be 'mini', 'large' + modelPath: '../models/emotion.json', }, embedding: { @@ -138,16 +137,17 @@ export default { body: { enabled: true, - modelPath: '../models/posenet.json', - inputSize: 257, // fixed value + modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose' + inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose maxDetections: 10, // maximum number of people detected in the input // should be set to the minimum number for performance + // only valid for posenet as blazepose only detects single pose scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score // in non-maximum suppression + // only valid for posenet as blazepose only detects single pose nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression - outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution - // defined by model itself, can be 8, 16, or 32 - modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet + // only valid for posenet as blazepose only detects single pose + modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose' }, hand: { diff --git a/demo/browser.js b/demo/browser.js index 350e6848..8b8fbcf9 100644 --- a/demo/browser.js +++ b/demo/browser.js @@ -14,10 +14,12 @@ const userConfig = {}; // add any user configuration overrides const userConfig = { backend: 'wasm', async: false, + warmup: 'full', videoOptimized: false, - face: { enabled: true, iris: { enabled: false }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } }, - body: { enabled: false }, - hand: { enabled: false }, + face: { enabled: true, iris: { enabled: true }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } }, + hand: { enabled: true }, + gestures: { enabled: true }, + body: { enabled: true, modelType: 'blazepose', modelPath: '../models/blazepose.json' }, }; */ @@ -31,15 +33,16 @@ const ui = { baseFontProto: 'small-caps {size} "Segoe UI"', baseLineWidth: 12, crop: true, - columns: 4, + columns: 2, busy: false, facing: true, useWorker: false, worker: 'worker.js', samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'], compare: '../assets/sample-me.jpg', + drawLabels: true, drawBoxes: true, - drawPoints: false, + drawPoints: true, drawPolygons: true, fillPolygons: false, useDepth: true, @@ -52,6 +55,7 @@ const ui = { detectFPS: [], drawFPS: [], buffered: false, + drawWarmup: false, drawThread: null, detectThread: null, framesDraw: 0, @@ -120,7 +124,7 @@ async function drawResults(input) { await menu.process.updateChart('FPS', ui.detectFPS); // get updated canvas - if (ui.buffered || !result.canvas) result.canvas = await human.image(input, userConfig); + if (ui.buffered || !result.canvas) result.canvas = await human.image(input).canvas; // draw image from video const ctx = canvas.getContext('2d'); @@ -436,6 +440,7 @@ function setupMenu() { }); menu.display.addHTML('
'); menu.display.addBool('use 3D depth', ui, 'useDepth'); + menu.display.addBool('print labels', ui, 'drawLabels'); menu.display.addBool('draw boxes', ui, 'drawBoxes'); menu.display.addBool('draw polygons', ui, 'drawPolygons'); menu.display.addBool('Fill Polygons', ui, 'fillPolygons'); @@ -530,6 +535,18 @@ function setupMenu() { document.getElementById('play').addEventListener('click', () => detectVideo()); } +async function drawWarmup(res) { + const canvas = document.getElementById('canvas'); + canvas.width = res.canvas.width; + canvas.height = res.canvas.height; + const ctx = canvas.getContext('2d'); + ctx.drawImage(res.canvas, 0, 0, res.canvas.width, res.canvas.height, 0, 0, canvas.width, canvas.height); + await draw.face(res.face, canvas, ui, human.facemesh.triangulation); + await draw.body(res.body, canvas, ui); + await draw.hand(res.hand, canvas, ui); + await draw.gesture(res.gesture, canvas, ui); +} + async function main() { log('Demo starting ...'); log('Browser:', navigator?.userAgent); @@ -543,7 +560,9 @@ async function main() { } if (!ui.useWorker) { status('initializing'); - await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference + const res = await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference + ui.baseFont = ui.baseFontProto.replace(/{size}/, '16px'); + if (res && res.canvas && ui.drawWarmup) await drawWarmup(res); } status('human: ready'); document.getElementById('loader').style.display = 'none'; diff --git a/demo/draw.js b/demo/draw.js index 1d6ce3d1..e2ca934d 100644 --- a/demo/draw.js +++ b/demo/draw.js @@ -1,3 +1,12 @@ +async function drawPoint(canvas, x = 0, y = 0, radius = 0, color = 'black', label) { + const ctx = canvas.getContext('2d'); + ctx.fillStyle = color; + ctx.beginPath(); + ctx.arc(x, y, radius, 0, 2 * Math.PI); + ctx.fill(); + if (label) ctx.fillText(label, x + 4, y + 4); +} + async function drawGesture(result, canvas, ui) { if (!result) return; const ctx = canvas.getContext('2d'); @@ -11,7 +20,9 @@ async function drawGesture(result, canvas, ui) { const label = `${where[0]} ${person}: ${what[1]}`; ctx.fillStyle = 'black'; ctx.fillText(label, 8, 2 + (i * ui.baseLineHeight)); + // ctx.fillText(label, 151, i * 16 + 101); ctx.fillStyle = ui.baseLabel; + // ctx.fillText(label, 150, i * 16 + 100); ctx.fillText(label, 6, 0 + (i * ui.baseLineHeight)); i += 1; } @@ -48,8 +59,10 @@ async function drawFace(result, canvas, ui, triangulation) { const x = Math.max(face.box[0], 0); const y = i * ui.baseLineHeight + face.box[1]; ctx.fillText(labels[i], x + 5, y + 16); + // ctx.fillText(labels[i], 151, i * 16 + 28); ctx.fillStyle = ui.baseLabel; ctx.fillText(labels[i], x + 4, y + 15); + // ctx.fillText(labels[i], 150, i * 16 + 27); } ctx.fillStyle = ui.baseColor; ctx.stroke(); @@ -119,13 +132,17 @@ async function drawBody(result, canvas, ui) { const ctx = canvas.getContext('2d'); ctx.lineJoin = 'round'; for (let i = 0; i < result.length; i++) { + // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5); if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] }; - ctx.fillStyle = ui.baseColor; ctx.strokeStyle = ui.baseColor; ctx.font = ui.baseFont; ctx.lineWidth = ui.baseLineWidth; if (ui.drawPoints) { for (let pt = 0; pt < result[i].keypoints.length; pt++) { + ctx.fillStyle = ui.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : ui.baseColor; + if (ui.drawLabels) { + ctx.fillText(`${result[i].keypoints[pt].part}`, result[i].keypoints[pt].position.x + 4, result[i].keypoints[pt].position.y + 4); + } ctx.beginPath(); if (ui.buffered) { lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2; @@ -162,6 +179,10 @@ async function drawBody(result, canvas, ui) { if (part) path.lineTo(part.position.x, part.position.y); part = result[i].keypoints.find((a) => a.part === 'leftAnkle'); if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'leftHeel'); + if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'leftFoot'); + if (part) path.lineTo(part.position.x, part.position.y); } // leg right root = result[i].keypoints.find((a) => a.part === 'rightHip'); @@ -171,6 +192,10 @@ async function drawBody(result, canvas, ui) { if (part) path.lineTo(part.position.x, part.position.y); part = result[i].keypoints.find((a) => a.part === 'rightAnkle'); if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'rightHeel'); + if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'rightFoot'); + if (part) path.lineTo(part.position.x, part.position.y); } // arm left root = result[i].keypoints.find((a) => a.part === 'leftShoulder'); @@ -180,6 +205,8 @@ async function drawBody(result, canvas, ui) { if (part) path.lineTo(part.position.x, part.position.y); part = result[i].keypoints.find((a) => a.part === 'leftWrist'); if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'leftPalm'); + if (part) path.lineTo(part.position.x, part.position.y); } // arm right root = result[i].keypoints.find((a) => a.part === 'rightShoulder'); @@ -189,6 +216,8 @@ async function drawBody(result, canvas, ui) { if (part) path.lineTo(part.position.x, part.position.y); part = result[i].keypoints.find((a) => a.part === 'rightWrist'); if (part) path.lineTo(part.position.x, part.position.y); + part = result[i].keypoints.find((a) => a.part === 'rightPalm'); + if (part) path.lineTo(part.position.x, part.position.y); } // draw all ctx.stroke(path); @@ -253,4 +282,5 @@ export default { body: drawBody, hand: drawHand, gesture: drawGesture, + point: drawPoint, }; diff --git a/demo/node.js b/demo/node.js index 07d75e3a..29442994 100644 --- a/demo/node.js +++ b/demo/node.js @@ -14,16 +14,19 @@ const myConfig = { videoOptimized: false, async: false, face: { - // detector: { modelPath: 'file://models/faceboxes.json' }, - detector: { modelPath: 'file://models/blazeface-back.json' }, // cannot use blazeface in nodejs due to missing required kernel function in tfjs-node - mesh: { modelPath: 'file://models/facemesh.json' }, - iris: { modelPath: 'file://models/iris.json' }, - age: { modelPath: 'file://models/age-ssrnet-imdb.json' }, - gender: { modelPath: 'file://models/gender.json' }, - emotion: { modelPath: 'file://models/emotion-large.json' }, + enabled: true, + detector: { modelPath: 'file://models/faceboxes.json', enabled: true, minConfidence: 0.5 }, + // detector: { modelPath: 'file://models/blazeface-back.json', enabled: false }, // cannot use blazeface in nodejs due to missing required kernel function in tfjs-node + mesh: { modelPath: 'file://models/facemesh.json', enabled: false }, // depends on blazeface detector + iris: { modelPath: 'file://models/iris.json', enabled: true }, + age: { modelPath: 'file://models/age-ssrnet-imdb.json', enabled: true }, + gender: { modelPath: 'file://models/gender.json', enabled: true }, + emotion: { modelPath: 'file://models/emotion.json', enabled: true }, }, - body: { modelPath: 'file://models/posenet.json' }, + // body: { modelPath: 'file://models/blazepose.json', modelType: 'blazepose', inputSize: 256, enabled: true }, + body: { modelPath: 'file://models/posenet.json', modelType: 'posenet', inputSize: 257, enabled: true }, hand: { + enabled: true, detector: { modelPath: 'file://models/handdetect.json' }, skeleton: { modelPath: 'file://models/handskeleton.json' }, }, @@ -35,7 +38,14 @@ async function init() { // create instance of human human = new Human(myConfig); // pre-load models + log.info('Human:', human.version); + log.info('Active Configuration', human.config); + log.info('TFJS Version:', human.tf.version_core, 'Backend:', tf.getBackend()); + log.info('TFJS Flags:', human.tf.env().features); await human.load(); + const loaded = Object.keys(human.models).filter((a) => human.models[a]); + log.info('Loaded:', loaded); + log.info('Memory state:', human.tf.engine().memory()); } async function detect(input) { @@ -74,6 +84,7 @@ async function test() { async function main() { log.info('NodeJS:', process.version); + log.info('Current folder:', process.env.PWD); await init(); if (process.argv.length !== 3) { log.warn('Parameters: missing'); diff --git a/dist/demo-browser-index.js b/dist/demo-browser-index.js index 5c94eae3..f07a6db5 100644 --- a/dist/demo-browser-index.js +++ b/dist/demo-browser-index.js @@ -5,31 +5,31 @@ author: ' */ -var h4=Object.create,nh=Object.defineProperty,d4=Object.getPrototypeOf,p4=Object.prototype.hasOwnProperty,f4=Object.getOwnPropertyNames,m4=Object.getOwnPropertyDescriptor,bf=e=>nh(e,"__esModule",{value:!0}),$2=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),kr=(e,t)=>{for(var n in t)nh(e,n,{get:t[n],enumerable:!0})},A4=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of f4(t))!p4.call(e,r)&&r!=="default"&&nh(e,r,{get:()=>t[r],enumerable:!(n=m4(t,r))||n.enumerable});return e},rh=e=>e&&e.__esModule?e:A4(bf(nh(e!=null?h4(d4(e)):{},"default",{value:e,enumerable:!0})),e),w4=$2(e=>{bf(e),kr(e,{MediaPipeFaceMesh:()=>t,load:()=>r});var t=class{constructor(a,s,i,o){this.facePipeline=new x4(a,s,i,o),this.config=o}async estimateFaces(a,s){let i=await this.facePipeline.predict(a,s),o=[];for(let l of i||[]){if(l.isDisposedInternal)continue;let u=l.coords?l.coords.arraySync():null,c=l.rawCoords,h={};if(u&&u.length>0)for(let f of Object.keys(ma))h[f]=ma[f].map(m=>u[m]);let 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KI(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),u=R(r,"data","basicLSTMCell"),c=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),d=ot([u,h],1),p=Ge(d,o),f=oe(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Fe(f,[0,0],y),w=Fe(f,[0,A],y),b=Fe(f,[0,A*2],y),_=Fe(f,[0,A*3],y),x=oe(W(Qn(g),Bo(w)),W(c,Qn(oe(i,b)))),N=W(Bo(x),Qn(_));return[x,N]}var U4=O({basicLSTMCell_:KI});function ZI(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(Zl,s,i)}var gu=O({batchToSpaceND_:ZI});function YI(e){let t;return e.rank===0||e.rank===1?t=j(e,[1,1,1,e.size]):e.rank===2?t=j(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=j(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function JI(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),u;a!=null&&(u=R(a,"scale","batchNorm"));let c;r!=null&&(c=R(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:YI(i),scale:u,offset:c,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(cs,h,d);return j(p,i.shape)}var js=O({batchNorm_:JI});function QI(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),u;a!=null&&(u=R(a,"scale","batchNorm"));let c;return r!=null&&(c=R(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),js(i,o,l,c,u,s)}var X2=O({batchNorm2d_:QI});function eN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),u;a!=null&&(u=R(a,"scale","batchNorm"));let c;return r!=null&&(c=R(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),js(i,o,l,c,u,s)}var K2=O({batchNorm3d_:eN});function tN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),u;a!=null&&(u=R(a,"scale","batchNorm"));let c;return r!=null&&(c=R(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),js(i,o,l,c,u,s)}var Z2=O({batchNorm4d_:tN});function nN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(ch,s,i)}var Y2=O({bincount_:nN});function rN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to 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Labels / logits was rank ${t.rank} and dim was ${n}`);return Tr((r,a,s)=>{let i=Xf(a,[n],!0),o=xe(ye(a,"float32"),i);s([r,o]);let l=bt(W(o,r));return{value:Ee(l,[n]),gradFunc:(u,c)=>{let[h,d]=c,p=ri(u.shape,[n]);return[W(j(u,p),xe(ye(h,"float32"),Un(d))),W(j(u,p),xe(Un(d),ye(h,"float32")))]}}})(e,t)}function JE(e,t,n,r=0,a=ln.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),nn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let u=ke(r),c=ke(1),h=ke(s.shape[1]);s=oe(W(s,xe(c,u)),ve(u,h))}let l=YE(s,i);return na(l,o,a)}var QE=O({softmaxCrossEntropy_:JE}),f8={fft:Cu,ifft:Ko,rfft:Ru,irfft:cd},m8={hammingWindow:JT,hannWindow:J5,frame:Q5,stft:nE},Nt={flipLeftRight:iE,resizeNearestNeighbor:sx,resizeBilinear:ax,rotateWithOffset:lE,cropAndResize:aE,nonMaxSuppression:cE,nonMaxSuppressionAsync:gE,nonMaxSuppressionWithScore:wE,nonMaxSuppressionWithScoreAsync:_E,nonMaxSuppressionPadded:kE,nonMaxSuppressionPaddedAsync:NE},w0={bandPart:CE,gramSchmidt:FE,qr:$E},A8={absoluteDifference:zE,computeWeightedLoss:na,cosineDistance:PE,hingeLoss:BE,huberLoss:UE,logLoss:jE,meanSquaredError:qE,sigmoidCrossEntropy:ZE,softmaxCrossEntropy:QE},Jr=class extends L5{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Re(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return o0(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Jr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var md=class extends Jr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:V(()=>He(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:V(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;V(()=>{let l=oe(W(i,this.rho),W(it(s),1-this.rho)),u=W(ve(Yt(oe(o,this.epsilon)),Yt(oe(i,this.epsilon))),s),c=oe(W(o,this.rho),W(it(u),1-this.rho));i.assign(l),o.assign(c);let h=oe(W(u,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};md.className="Adadelta";Fa(md);var Ad=class extends Jr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:V(()=>bu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;V(()=>{let i=oe(s,it(a));s.assign(i);let o=oe(W(ve(a,Yt(oe(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Ad.className="Adagrad";Fa(Ad);var yd=class extends Jr{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=xe(1,this.accBeta1),r=xe(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:V(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:V(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedSecondMoment[s].variable,h=oe(W(u,this.beta1),W(l,1-this.beta1)),d=oe(W(c,this.beta2),W(it(l),1-this.beta2)),p=ve(h,n),f=ve(d,r);u.assign(h),c.assign(d);let m=oe(W(ve(p,oe(Yt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(W(this.accBeta1,this.beta1)),this.accBeta2.assign(W(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Yr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};yd.className="Adam";Fa(yd);var gd=class extends Jr{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=xe(1,this.accBeta1),r=ve(-this.learningRate,oe(W(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};gd.className="Adamax";Fa(gd);var Fu=class extends Jr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];V(()=>{let s=oe(W(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Vt(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};xd.className="Momentum";Fa(xd);var wd=class extends Jr{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:V(()=>He(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:V(()=>He(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:V(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;V(()=>{let l=oe(W(i,this.decay),W(it(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,c=oe(W(u,this.decay),W(s,1-this.decay)),h=ve(W(s,this.learningRate),Yt(xe(l,oe(it(c),this.epsilon)))),d=oe(W(o,this.momentum),h);i.assign(l),u.assign(c),o.assign(d);let p=xe(r,d);r.assign(p)}else{let u=oe(W(i,this.decay),W(it(s),1-this.decay)),c=oe(W(o,this.momentum),ve(W(s,this.learningRate),Yt(oe(u,this.epsilon))));i.assign(u),o.assign(c);let h=xe(r,c);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,w=c.dilationWidth,b=c.effectiveFilterDepth,_=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,T=x-1-c.padInfo.left,E=_-1-c.padInfo.top,$=We(s.shape,"float32"),D=1/(f*m*A),L=n.bufferSync(a);for(let P=0;P=c.outDepth||Math.floor(Q)!==Q))for(let de=0;de<_;de+=g){let ue=(J+de)/d;if(!(ue<0||ue>=c.outHeight||Math.floor(ue)!==ue))for(let me=0;me=c.outWidth||Math.floor(fe)!==fe||(te+=L.get(P,Q,ue,fe,U))}}}$.set(te*D,P,j,X,G,U)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var tF={kernelName:lh,backendName:"cpu",kernelFunc:eF};function nF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;_e([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,w=g-1-c.padInfo.left,b=y-1-c.padInfo.top,_=We(i.shape,"float32"),x=1/(p*f),N=n.data.get(a.dataId).values,T=We(a.shape,"float32",N);for(let E=0;E=c.outHeight||Math.floor(G)!==G))for(let ee=0;ee=c.outWidth||Math.floor(J)!==J||(j+=T.get(E,G,J,$))}}_.set(j*x,E,D,L,$)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var rF={kernelName:oh,backendName:"cpu",kernelFunc:nF};function aF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),f=sr({inputs:{x:p},backend:n,attrs:{perm:u}}),m=At({inputs:{x:f},backend:n,attrs:{shape:c}}),A=ri({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var oF={kernelName:ql,backendName:"cpu",kernelFunc:iF};function lF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=eA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var uF={kernelName:uh,backendName:"cpu",kernelFunc:lF},cF=at(ya,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new 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At({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(u.map(m=>m.shape),1);let h=u[0].shape[0]===1,d=tA(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var mF={kernelName:ji,backendName:"cpu",kernelFunc:cl};function Bx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;_e([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,w=d.dataFormat==="channelsLast",b=new Mt(d.outShape,a.dtype),_=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=_[0],T=w?_[1]:_[2],E=w?_[2]:1,$=w?1:_[1],D=b.strides[0],L=w?b.strides[1]:b.strides[2],P=w?b.strides[2]:1,U=w?1:b.strides[1],j=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let me=de*x[0],fe=J+ue*T;for(let Ie=0;Ie=d.inWidth)continue;let et=me+Oe*x[1],tt=fe+Me*E,st=et;for(let Ke=0;Ke=u.inDepth)continue;let ee=X*E[0],J=D+G*T[1];for(let se=0;se=u.inHeight)continue;let ue=ee+Q*E[1],me=J+de*T[2];for(let fe=0;fe=u.inWidth)continue;let Me=ue+$e*E[2],et=me+Oe*u.inChannels,tt=Me;for(let st=0;stMath.cos(e)),TF={kernelName:ts,backendName:"cpu",kernelFunc:SF},EF=at(Hi,e=>Math.cosh(e)),CF={kernelName:Hi,backendName:"cpu",kernelFunc:EF};function RF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,_=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=m>1?(D-E)*(h-1)/(m-1):0,j=A>1?(L-$)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?$*(d-1)+te*j:.5*($+L)*(d-1);if(le<0||le>d-1){for(let me=0;me1?$*(d-1)+ee*j:.5*($+L)*(d-1);if(J<0||J>d-1){for(let le=0;ley+f-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),A=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=p,w=g.left,b=g.top,_=p.outChannels/p.inChannels,x=new Mt(p.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=x.values;for(let $=0;$=p.inHeight)continue;let ee=X*h[0],J=D+G*c[1];for(let se=0;se=p.inWidth)continue;let ue=ee+Q*h[1],me=J+de*p.inChannels,fe=te,Ie=ue;for(let Se=0;Se{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:w,strideHeight:b,strideWidth:_,filterHeight:x,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:$}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),D=v.sizeFromShape($),L=$.length,P=v.getArrayFromDType(r.dtype,D);for(let U=0;U=0&&Q=0&&uese&&(se=Ie)}}}let 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At({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(u.map(m=>m.shape),1);let h=u[0].shape[0]===1,d=nA(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var bF={kernelName:qi,backendName:"cpu",kernelFunc:dl};function Hx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;_e([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,w=d.dataFormat==="channelsLast",b=new $t(d.outShape,a.dtype),_=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=_[0],T=w?_[1]:_[2],E=w?_[2]:1,M=w?1:_[1],D=b.strides[0],L=w?b.strides[1]:b.strides[2],P=w?b.strides[2]:1,U=w?1:b.strides[1],H=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let Ae=de*x[0],fe=J+ue*T;for(let Ie=0;Ie=d.inWidth)continue;let et=Ae+Oe*x[1],tt=fe+$e*E,st=et;for(let Ke=0;Ke=u.inDepth)continue;let ee=X*E[0],J=D+G*T[1];for(let ie=0;ie=u.inHeight)continue;let ue=ee+Q*E[1],Ae=J+de*T[2];for(let fe=0;fe=u.inWidth)continue;let $e=ue+Me*E[2],et=Ae+Oe*u.inChannels,tt=$e;for(let st=0;stMath.cos(e)),$F={kernelName:rs,backendName:"cpu",kernelFunc:MF},DF=at(Xi,e=>Math.cosh(e)),OF={kernelName:Xi,backendName:"cpu",kernelFunc:DF};function zF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,_=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=m>1?(D-E)*(h-1)/(m-1):0,H=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*H:.5*(M+L)*(d-1);if(le<0||le>d-1){for(let Ae=0;Ae1?M*(d-1)+ee*H:.5*(M+L)*(d-1);if(J<0||J>d-1){for(let le=0;ley+f-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. 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NM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=Hx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=qu({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=uA(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var SM={kernelName:Vs,backendName:"cpu",kernelFunc:NM};function TM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=jx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=qu({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=uA(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var EM={kernelName:Us,backendName:"cpu",kernelFunc:TM};function CM(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=C.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=We([u,c],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;ge>=t?1:0),DM=jt(hs,$M,null,"bool"),OM={kernelName:hs,backendName:"cpu",kernelFunc:DM};function zM(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=At({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=qx(o,!0,n),u=At({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var LM={kernelName:vh,backendName:"cpu",kernelFunc:zM},PM=at(io,e=>Number.isFinite(e)?1:0,"bool"),WM={kernelName:io,backendName:"cpu",kernelFunc:PM},BM=at(oo,e=>Math.abs(e)===Infinity?1:0,"bool"),VM={kernelName:oo,backendName:"cpu",kernelFunc:BM},UM=at(lo,e=>Number.isNaN(e)?1:0,"bool"),HM={kernelName:lo,backendName:"cpu",kernelFunc:UM},jM=Rt((e,t)=>e<=t?1:0),GM=jt(co,jM,null,"bool"),qM={kernelName:co,backendName:"cpu",kernelFunc:GM};function XM(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=wx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var KM={kernelName:Ih,backendName:"cpu",kernelFunc:XM},ZM=at(ho,e=>Math.log1p(e)),YM={kernelName:ho,backendName:"cpu",kernelFunc:ZM},JM=Rt((e,t)=>e&&t),QM=jt(po,JM,null,"bool"),e$={kernelName:po,backendName:"cpu",kernelFunc:QM},t$=at(tu,e=>e?0:1,"bool"),n$={kernelName:tu,backendName:"cpu",kernelFunc:t$},r$=Rt((e,t)=>e||t),a$=jt(nu,r$,null,"bool"),s$={kernelName:nu,backendName:"cpu",kernelFunc:a$};function i$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;_e(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%u,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,c),w=0;for(;y<=g;y++){let b=h[y];w+=b*b}return w}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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Ce=Y();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>yA(2)?2:yA(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>gw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>xw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:ww(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Ph.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>bw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>_w(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>vw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function un(){let e,t,n,r,a,s,i,o,l,u;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=` bool isnan_custom(float val) { return (val > 0.0 || val < 0.0) ? false : val != 0.0; } @@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function oi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function wA(e){let t=v.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function ci(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function bA(e){let t=v.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}var _w=` +`}var Iw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return c / 255.0; } -`,gO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Xu.DENSE;let t=Zu(e),n=un();this.outputShape=e,this.userCode=` +`,kO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Yu.DENSE;let t=Qu(e),n=un();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${ci(["r","c","d"],e)} return ivec3(r, c, d); } @@ -138,9 +138,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},xO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Xu.DENSE;let t=Zu(e),n=un();this.outputShape=e,this.userCode=` + `}},IO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Yu.DENSE;let t=Qu(e),n=un();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${ci(["r","c","d"],e)} return ivec3(r, c, d); } @@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},wO=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Gn.DOWNLOAD;let t=un();this.outputShape=e,this.userCode=` - ${_w} + `}},NO=class{constructor(e){this.variableNames=["A"],this.outTexUsage=qn.DOWNLOAD;let t=un();this.outputShape=e,this.userCode=` + ${Iw} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},bO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Gn.DOWNLOAD;let t=un();this.outputShape=e,this.userCode=` - ${_w} + `}},SO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=qn.DOWNLOAD;let t=un();this.outputShape=e,this.userCode=` + ${Iw} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},_O=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=un(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` - ${wA(e)} + `}},TO=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=un(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` + ${bA(e)} void main() { ivec3 coords = getOutputCoords(); @@ -204,7 +204,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${r.output} = vec4(${i}, 0., 0., 0.); } - `}},vO=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=un(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` + `}},EO=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=un(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` localCoords = coords; if(localCoords[2] + ${u} < ${e[2]}) { localCoords[2] += ${u}; @@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee } } `}this.userCode=` - ${wA(e)} + ${bA(e)} void main() { ivec3 coords = getOutputCoords(); @@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. 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Looks like you are running TensorFlow.js in Node.js. 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uv).r; } - `}function EO(e){return` + `}function DO(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function CO(e){return` + `}function OO(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function RO(e){return`${e.version} + `}function zO(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return fract((p3.x + p3.y) * p3.z); } - ${az} - ${sz} - ${iz} - `}var az=` + ${cz} + ${hz} + ${dz} + `}var cz=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,sz=` +`,hz=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,iz=` +`,dz=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,FO=` +`,LO=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? 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release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Zz(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Zw(e,t,n,r,a){let s=Yz(t,r),i;if(a){let[l,u]=fl(e[0],e[1]);i=l*u}else{let[l,u]=Ju(e[0],e[1]);i=l*u}let o=Zz(n,s);return i*o}function Yz(e,t){switch(e){case Jt.PACKED_2X2_FLOAT32:return IA(t);case Jt.PACKED_2X2_FLOAT16:return NA(t);case Jt.UNPACKED_FLOAT32:return _A(t);case Jt.UNPACKED_FLOAT16:return vA(t);case Jt.PACKED_4X1_UNSIGNED_BYTE:return kA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Jz(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Jt.PACKED_2X2_FLOAT32:Jt.UNPACKED_FLOAT32:e?Jt.PACKED_2X2_FLOAT16:Jt.UNPACKED_FLOAT16}function Yw(e,t){if(e===qn.UPLOAD)return Jt.PACKED_2X2_FLOAT32;if(e===qn.RENDER||e==null)return Jz(t);if(e===qn.DOWNLOAD||e===qn.PIXELS)return Jt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Jw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Da=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=` float unaryOperation(float x) { ${t} } @@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},mr="if (isnan(x)) return x;",qz="return x;",Zw="return abs(x);",Xz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Kz=mr+` + `}},yr="if (isnan(x)) return x;",Qz="return x;",Qw="return abs(x);",eL="return (x >= 0.0) ? x : (exp(x) - 1.0);",tL=yr+` return (x < 0.0) ? 0.0 : x; -`,Zz=mr+` +`,nL=yr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Kd="return x;",Yz="return x;",Jz=` +`,Zd="return x;",rL="return x;",aL=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Qz=` +`,sL=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,eL=` +`,iL=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,yl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` +`,xl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},tL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=cn("rc",t),r=lt(t),a=Oz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},oL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=cn("rc",t),r=lt(t),a=Vz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${r} rc = getOutputCoords(); vec4 packedInput = getA(${a}); setOutput(getChannel(packedInput, ${i})); } - `}},nL=Rr.whereImpl,rL=1e-7,aL=1e-4,SA={};function sL(e){return e in SA||(SA[e]={}),SA[e]}var iL=128,oL=600;function lL(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*oL/1024/1024}var Cu=class extends Ul{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Lr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=sL(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new hm(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Uz(this.gpgpu),this.numMBBeforeWarning=lL(),this.texData=new ah(this,Nr())}nextDataId(){return Cu.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Gn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Gn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new yl(i,Kd):h=new $a(i,Kd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new yl(r,Kd):p=new $a(r,Kd);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Zu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=C.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Nr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return Y().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Nr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=iL){let n=this.getCPUBackend();return!Y().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Nr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new tL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Wz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ai(e.shape),...si(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[ai(t),...si(t)],s=new Gw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Hd(r),i;n?i=new xO(s):i=new gO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Xu.DENSE){let m=Zu(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!qu(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=uz(e,l,u),h=this.getAndSaveBinary(c,()=>oz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),lz(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?rL:aL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=fw(n,o),t.texShape=c),a!=null){let h=Hd(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=dl(c[0],c[1]),d=new vO(h,[f,p],m)):d=new _O(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Gn.PIXELS:this.texData.get(A.dataId).usage=Gn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=uL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Cu.nextDataId=0;function uL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew Cu,2);var h8={forceHalfFloat:_0},Yw=` + `}},lL=Fr.whereImpl,uL=1e-7,cL=1e-4,TA={};function hL(e){return e in TA||(TA[e]={}),TA[e]}var dL=128,pL=600;function fL(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*pL/1024/1024}var Mu=class extends Gl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Pr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=hL(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new dm(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Kz(this.gpgpu),this.numMBBeforeWarning=fL(),this.texData=new sh(this,Sr())}nextDataId(){return Mu.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:qn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:qn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new xl(i,Zd):h=new Da(i,Zd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new xl(r,Zd):p=new Da(r,Zd);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Qu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=C.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Sr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return Y().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Sr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=dL){let n=this.getCPUBackend();return!Y().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Sr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new oL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Gz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[oi(e.shape),...li(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[oi(t),...li(t)],s=new Kw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Gd(r),i;n?i=new IO(s):i=new kO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Yu.DENSE){let m=Qu(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!Zu(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=mz(e,l,u),h=this.getAndSaveBinary(c,()=>pz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),fz(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?uL:cL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=yw(n,o),t.texShape=c),a!=null){let h=Gd(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=fl(c[0],c[1]),d=new EO(h,[f,p],m)):d=new TO(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=qn.PIXELS:this.texData.get(A.dataId).usage=qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=mL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Mu.nextDataId=0;function mL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew Mu,2);var y8={forceHalfFloat:I0},eb=` if (isnan(a)) return a; if (isnan(b)) return b; -`,gl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` +`,wl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -952,12 +952,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},Zd=` + `}},Yd=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`,Ju=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` +`,tc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function $n(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var cL={kernelName:cs,backendName:"webgl",kernelFunc:$n};function Ma(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=$n({inputs:{x:r},backend:n}),l=$n({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var hL={kernelName:ch,backendName:"webgl",kernelFunc:Ma},Jw="return (a < 0.) ? b * a : a;",Qw=` + `}};function Mn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var AL={kernelName:ds,backendName:"webgl",kernelFunc:Mn};function Oa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Mn({inputs:{x:r},backend:n}),l=Mn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var yL={kernelName:hh,backendName:"webgl",kernelFunc:Oa},tb="return (a < 0.) ? b * a : a;",nb=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function dL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(Qw,a.shape,i.shape):new gl(Jw,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var pL={kernelName:hs,backendName:"webgl",kernelFunc:dL},eb="return (a < 0.) ? b * a : a;",tb=` +`;function gL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(nb,a.shape,i.shape):new wl(tb,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var xL={kernelName:ps,backendName:"webgl",kernelFunc:gL},rb="return (a < 0.) ? b * a : a;",ab=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function fL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(tb,r.shape,a.shape):new gl(eb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var mL={kernelName:vs,backendName:"webgl",kernelFunc:fL},nb="if (isnan(x)) return x;",AL=` +`;function wL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(ab,r.shape,a.shape):new wl(rb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var bL={kernelName:Is,backendName:"webgl",kernelFunc:wL},sb="if (isnan(x)) return x;",_L=` if (isnan(a)) return a; if (isnan(b)) return b; -`,yL=` +`,vL=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new yl(i.shape,t):c=new $a(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new gl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Jn(b.dtype,_.dtype))}),g=Ma({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Jn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),w=c.texData.get(g.dataId);return w.values=A,g}let d=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new Ju(t,l.shape,u.shape,n):p=new gl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Yd(e,t=!1){if(e==="linear")return t?Yz:qz;if(e==="relu")return t?Qz:Kz;if(e==="elu")return t?Jz:Xz;if(e==="relu6")return t?eL:Zz;if(e==="prelu")return t?tb:eb;if(e==="leakyrelu")return t?Qw:Jw;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var rb=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) { +`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new xl(i.shape,t):c=new Da(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new wl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Jn(b.dtype,_.dtype))}),g=Oa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Jn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),w=c.texData.get(g.dataId);return w.values=A,g}let d=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new tc(t,l.shape,u.shape,n):p=new wl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Jd(e,t=!1){if(e==="linear")return t?rL:Qz;if(e==="relu")return t?sL:tL;if(e==="elu")return t?aL:eL;if(e==="relu6")return t?iL:nL;if(e==="prelu")return t?ab:rb;if(e==="leakyrelu")return t?nb:tb;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var ib=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?m=`vec4 activation(vec4 a) { @@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},ab={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},sb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` + `}},ob={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},lb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},ib="return a * b;";function ob(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new sb(ab.REAL,r.shape,a.shape),c=new sb(ab.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Ma({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Iz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ju(ib,r.shape,a.shape):i=new gl(ib,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var gL={kernelName:xs,backendName:"webgl",kernelFunc:ob};function xL(e,t,n){let r=[ai(e.shape),...si(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[ai(t),...si(t)],i=new Gw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ge(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!qu(a.shape,l)&&!(c.texture!==null&&qu(c.shape,l))?xL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var wL={kernelName:bo,backendName:"webgl",kernelFunc:ge},lb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` + `}},ub="return a * b;";function cb(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new lb(ob.REAL,r.shape,a.shape),c=new lb(ob.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Oa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Rz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new tc(ub,r.shape,a.shape):i=new wl(ub,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var kL={kernelName:bs,backendName:"webgl",kernelFunc:cb};function IL(e,t,n){let r=[oi(e.shape),...li(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[oi(t),...li(t)],i=new Kw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ge(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!Zu(a.shape,l)&&!(c.texture!==null&&Zu(c.shape,l))?IL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var NL={kernelName:ko,backendName:"webgl",kernelFunc:ge},hb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` if (inIdx < 0 || inIdx >= ${a}) { return 0.0; } @@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},bL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` + `}},SL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function _L(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ui(e,t,n,r){let a=_L(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=Hw("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=Xw("rc",this.rank),s=new Array(this.rank);for(let u=0;u=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=ge({inputs:{x:e},backend:a,attrs:{shape:_}}),T=ge({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],$=Math.max(y,g),D=n?N.shape[1]:N.shape[2],L=s!=null,P=i!=null,U=l==="leakyrelu",j=l!=null?Yd(l,!0):null,X=L||P||U||j!=null,G;if((p===1||f===1)&&D>ub&&X===!1){let J=N,se=T;n&&(J=wn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(J)),r&&(se=wn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(se));let te=f!==1,le=f===1,Q=J;te&&(Q=ge({inputs:{x:J},backend:a,attrs:{shape:[$,D,1]}}),E.push(Q));let de=f===1?2:1,ue=se;le&&(ue=ge({inputs:{x:se},backend:a,attrs:{shape:[$,1,D]}}),E.push(ue));let me=ob({inputs:{a:Q,b:ue},backend:a});G=TA({inputs:{x:me},backend:a,attrs:{axis:de,keepDims:!0}}),E.push(me)}else{let J=Jn(e.dtype,t.dtype),se=new rb(_,x,[$,p,f],n,r,L,j,P,U),te=[N,T];if(s!=null&&te.push(s),P&&te.push(i),U){let le=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(le),E.push(le)}G=a.runWebGLProgram(se,te,J)}let ee=ge({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let J of E)a.disposeIntermediateTensorInfo(J);return ee}function EL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Qd({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var CL={kernelName:Ps,backendName:"webgl",kernelFunc:EL},cb="return abs(x);";function RL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=jw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,cb):a=new $a(r.shape,cb),n.runWebGLProgram(a,[r],r.dtype)}var FL={kernelName:Oi,backendName:"webgl",kernelFunc:RL},$L=mr+` + `}};function Qd(e,t,n){let r=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new RL(e.shape,t):new CL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function FL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=Qd(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=ge({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=Lh(e.dtype),g=di(A,y,"sum",r),w=ge({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),w}function EA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return FL(a,s,i,n)}var ML={kernelName:Ds,backendName:"webgl",kernelFunc:EA};function wn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=ge({inputs:{x:e},backend:a,attrs:{shape:_}}),T=ge({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],M=Math.max(y,g),D=n?N.shape[1]:N.shape[2],L=s!=null,P=i!=null,U=l==="leakyrelu",H=l!=null?Jd(l,!0):null,X=L||P||U||H!=null,G;if((p===1||f===1)&&D>db&&X===!1){let J=N,ie=T;n&&(J=wn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(J)),r&&(ie=wn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ie));let te=f!==1,le=f===1,Q=J;te&&(Q=ge({inputs:{x:J},backend:a,attrs:{shape:[M,D,1]}}),E.push(Q));let de=f===1?2:1,ue=ie;le&&(ue=ge({inputs:{x:ie},backend:a,attrs:{shape:[M,1,D]}}),E.push(ue));let Ae=cb({inputs:{a:Q,b:ue},backend:a});G=EA({inputs:{x:Ae},backend:a,attrs:{axis:de,keepDims:!0}}),E.push(Ae)}else{let J=Jn(e.dtype,t.dtype),ie=new ib(_,x,[M,p,f],n,r,L,H,P,U),te=[N,T];if(s!=null&&te.push(s),P&&te.push(i),U){let le=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(le),E.push(le)}G=a.runWebGLProgram(ie,te,J)}let ee=ge({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let J of E)a.disposeIntermediateTensorInfo(J);return ee}function DL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return ep({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var OL={kernelName:Bs,backendName:"webgl",kernelFunc:DL},pb="return abs(x);";function zL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=qw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new xl(r.shape,pb):a=new Da(r.shape,pb),n.runWebGLProgram(a,[r],r.dtype)}var LL={kernelName:Pi,backendName:"webgl",kernelFunc:zL},PL=yr+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,ML=Xe({opSnippet:$L}),DL={kernelName:zi,backendName:"webgl",kernelFunc:ML},OL=mr+` +`,WL=Xe({opSnippet:PL}),BL={kernelName:Wi,backendName:"webgl",kernelFunc:WL},VL=yr+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,zL=Xe({opSnippet:OL}),LL={kernelName:Li,backendName:"webgl",kernelFunc:zL},hb="return a + b;",PL=Qt({opSnippet:hb,packedOpSnippet:hb,supportsComplex:!0,cpuKernelImpl:cz}),WL={kernelName:Aa,backendName:"webgl",kernelFunc:PL},BL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,UL=Xe({opSnippet:VL}),HL={kernelName:Bi,backendName:"webgl",kernelFunc:UL},fb="return a + b;",jL=Qt({opSnippet:fb,packedOpSnippet:fb,supportsComplex:!0,cpuKernelImpl:Az}),GL={kernelName:ga,backendName:"webgl",kernelFunc:jL},qL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,zL=Xe({opSnippet:OL}),LL={kernelName:Li,back float result = ${r}; setOutput(result); } - `}},VL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` + `}},XL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,zL=Xe({opSnippet:OL}),LL={kernelName:Li,back vec4 result = ${r}; setOutput(result); } - `}};function ep(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return $n({inputs:{x:r[0]},backend:n});if(r.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=ep({inputs:r.slice(0,o),backend:n}),u=ep({inputs:r.slice(o),backend:n});return ep({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Jn(o,l)),s=r.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new VL(r[0].shape,s):new BL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var UL={kernelName:qa,backendName:"webgl",kernelFunc:ep};function jL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=wn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=ui(m,m.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var HL={kernelName:sh,backendName:"webgl",kernelFunc:jL};function GL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=wn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=ui(m,m.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var qL={kernelName:ih,backendName:"webgl",kernelFunc:GL},XL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function tp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Mn({inputs:{x:r[0]},backend:n});if(r.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=tp({inputs:r.slice(0,o),backend:n}),u=tp({inputs:r.slice(o),backend:n});return tp({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Jn(o,l)),s=r.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new XL(r[0].shape,s):new qL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var KL={kernelName:Ka,backendName:"webgl",kernelFunc:tp};function ZL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=wn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=di(m,m.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var YL={kernelName:ih,backendName:"webgl",kernelFunc:ZL};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=wn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=di(m,m.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var QL={kernelName:oh,backendName:"webgl",kernelFunc:JL},eP=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1258,7 +1258,7 @@ return log(x + sqrt(x * x - 1.0));`,zL=Xe({opSnippet:OL}),LL={kernelName:Li,back } setOutput(float(bestIndex)); } - `}},KL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),u=cn("coords",o),c,h;if(s===1){h=o+1;let N=lt(h);c=` + `}},tP=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),u=cn("coords",o),c,h;if(s===1){h=o+1;let N=lt(h);c=` ${N} sourceLocR = ${N}(${u.join()}, 0); ++${u[o-1]}; ${N} sourceLocG = ${N}(${u.join()}, 0); @@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,zL=Xe({opSnippet:OL}),LL={kernelName:Li,back } setOutput(bestIndex); } - `}};function db(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new XL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=db(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function pb(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new KL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=pb(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function fb(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=ge({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=db(e,u,r);s.push(c);let h=ge({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return pb(e,t,r)}function ZL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=wn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=fb(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var YL={kernelName:Xa,backendName:"webgl",kernelFunc:ZL};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=wn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=fb(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var QL={kernelName:Hl,backendName:"webgl",kernelFunc:JL},eP=mr+` + `}};function mb(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new eP(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=mb(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function Ab(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new tP(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=Ab(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function yb(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=ge({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=mb(e,u,r);s.push(c);let h=ge({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return Ab(e,t,r)}function nP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=wn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=yb(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var rP={kernelName:Za,backendName:"webgl",kernelFunc:nP};function aP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=wn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=yb(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var sP={kernelName:Xl,backendName:"webgl",kernelFunc:aP},iP=yr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,tP=Xe({opSnippet:eP}),nP={kernelName:Pi,backendName:"webgl",kernelFunc:tP},rP=mr+"return log(x + sqrt(x * x + 1.0));",aP=Xe({opSnippet:rP}),sP={kernelName:Wi,backendName:"webgl",kernelFunc:aP},iP=mr+` +`,oP=Xe({opSnippet:iP}),lP={kernelName:Vi,backendName:"webgl",kernelFunc:oP},uP=yr+"return log(x + sqrt(x * x + 1.0));",cP=Xe({opSnippet:uP}),hP={kernelName:Ui,backendName:"webgl",kernelFunc:cP},dP=yr+` return atan(x); -`,oP=Xe({opSnippet:iP}),lP={kernelName:Bi,backendName:"webgl",kernelFunc:oP},uP=AL+` +`,pP=Xe({opSnippet:dP}),fP={kernelName:Hi,backendName:"webgl",kernelFunc:pP},mP=_L+` return atan(a, b); -`,cP=` +`,AP=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); - `+yL+` + `+vL+` return result; -`,hP=Qt({opSnippet:uP,packedOpSnippet:cP}),dP={kernelName:Ui,backendName:"webgl",kernelFunc:hP},pP=mr+` +`,yP=Qt({opSnippet:mP,packedOpSnippet:AP}),gP={kernelName:Gi,backendName:"webgl",kernelFunc:yP},xP=yr+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelName:Vi,backendName:"webgl",kernelFunc:fP},Qu=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wP=Xe({opSnippet:xP}),bP={kernelName:ji,backendName:"webgl",kernelFunc:wP},nc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); @@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(${w}); } - `}},EA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=` + `}},CA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${y}); @@ -1640,7 +1640,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput(${_}); } } - `}};function AP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return $n({inputs:{x:a},backend:n});let h=new Qu(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var yP={kernelName:Ka,backendName:"webgl",kernelFunc:AP};function gP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new EA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var xP={kernelName:Gl,backendName:"webgl",kernelFunc:gP},wP=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` + `}};function _P(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;pl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Mn({inputs:{x:a},backend:n});let h=new nc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var vP={kernelName:Ya,backendName:"webgl",kernelFunc:_P};function kP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new CA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var IP={kernelName:Kl,backendName:"webgl",kernelFunc:kP},NP=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${h}); @@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},bP=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` + `}},SP=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${p}, ${f}, ${m}); const float avgMultiplier = float(${A}); @@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}};function _P(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new bP(d);return n.runWebGLProgram(p,[a],i.dtype)}var vP={kernelName:lh,backendName:"webgl",kernelFunc:_P};function kP(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;hl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new wP(c);return n.runWebGLProgram(h,[a],i.dtype)}var IP={kernelName:oh,backendName:"webgl",kernelFunc:kP};function NP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Qd({a,b:s,transposeA:i,transposeB:o,backend:n})}var SP={kernelName:Za,backendName:"webgl",kernelFunc:NP},TP=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function TP(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new SP(d);return n.runWebGLProgram(p,[a],i.dtype)}var EP={kernelName:uh,backendName:"webgl",kernelFunc:TP};function CP(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;pl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new NP(c);return n.runWebGLProgram(h,[a],i.dtype)}var RP={kernelName:lh,backendName:"webgl",kernelFunc:CP};function FP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return ep({a,b:s,transposeA:i,transposeB:o,backend:n})}var MP={kernelName:Ja,backendName:"webgl",kernelFunc:FP},$P=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},EP=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},DP=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput((x - mean) * inv + offset); } - `}},CP=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=Y().getBool("WEBGL_PACK_NORMALIZATION")?new EP(r.shape,a.shape,s.shape,c,h,l):new TP(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},RP={kernelName:ls,backendName:"webgl",kernelFunc:CP},$P=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=FP(this.rank),a,s=e.map((i,o)=>`sourceLoc.${CA[o]} = start[${o}] + coords.${CA[o]};`);a=` + `}},OP=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=Y().getBool("WEBGL_PACK_NORMALIZATION")?new DP(r.shape,a.shape,s.shape,c,h,l):new $P(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},zP={kernelName:cs,backendName:"webgl",kernelFunc:OP},PP=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=LP(this.rank),a,s=e.map((i,o)=>`sourceLoc.${RA[o]} = start[${o}] + coords.${RA[o]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` @@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${a} setOutput(getSource(${r})); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},CA=["x","y","z","w","u","v"];function FP(e){if(e===1)return"sourceLoc";if(e<=6)return CA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var MP=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=cn("coords",this.rank),r=cn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},RA=["x","y","z","w","u","v"];function LP(e){if(e===1)return"sourceLoc";if(e<=6)return RA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var WP=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=cn("coords",this.rank),r=cn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; @@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${o} setOutput(result); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function DP(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=sn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function ec(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=sn.parseSliceParams(a,s,i);if(sn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Cz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=sn.isSliceContinous(a.shape,o,l);if(u||!c){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MP(l):new $P(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),DP(a,o,l,n)}var OP={kernelName:Io,backendName:"webgl",kernelFunc:ec},zP=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],f=ge({inputs:{x:a},backend:n,attrs:{shape:l}}),m=wn({inputs:{x:f},backend:n,attrs:{perm:u}}),A=ge({inputs:{x:m},backend:n,attrs:{shape:c}}),y=ec({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},LP={kernelName:ql,backendName:"webgl",kernelFunc:zP};function PP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Uw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var WP={kernelName:uh,backendName:"webgl",kernelFunc:PP},BP="return float(a != b);",mb=Qt({opSnippet:BP,dtype:"bool"}),VP={kernelName:po,backendName:"webgl",kernelFunc:mb};function tc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return $n({inputs:{x:a.complexTensorInfos.real},backend:n})}var UP={kernelName:Ch,backendName:"webgl",kernelFunc:tc},jP="return float(int(x));";function HP(e,t){let n=new $a(e.shape,jP),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function RA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return $n({inputs:{x:a},backend:n});let i=Ct(a.shape),o=RA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ma({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=tc({inputs:{input:a},backend:n}),o=RA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=$n({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return HP(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=mb({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var GP={kernelName:Ya,backendName:"webgl",kernelFunc:RA},Ab="return ceil(x);",qP=Xe({opSnippet:Ab,packedOpSnippet:Ab,cpuKernelImpl:dz}),XP={kernelName:Ja,backendName:"webgl",kernelFunc:qP},KP=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function BP(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=sn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function rc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=sn.parseSliceParams(a,s,i);if(sn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Oz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=sn.isSliceContinous(a.shape,o,l);if(u||!c){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WP(l):new PP(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),BP(a,o,l,n)}var VP={kernelName:To,backendName:"webgl",kernelFunc:rc},UP=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],f=ge({inputs:{x:a},backend:n,attrs:{shape:l}}),m=wn({inputs:{x:f},backend:n,attrs:{perm:u}}),A=ge({inputs:{x:m},backend:n,attrs:{shape:c}}),y=rc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},HP={kernelName:Zl,backendName:"webgl",kernelFunc:UP};function jP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Gw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var GP={kernelName:ch,backendName:"webgl",kernelFunc:jP},qP="return float(a != b);",gb=Qt({opSnippet:qP,dtype:"bool"}),XP={kernelName:Ao,backendName:"webgl",kernelFunc:gb};function ac(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Mn({inputs:{x:a.complexTensorInfos.real},backend:n})}var KP={kernelName:Rh,backendName:"webgl",kernelFunc:ac},ZP="return float(int(x));";function YP(e,t){let n=new Da(e.shape,ZP),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function FA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Mn({inputs:{x:a},backend:n});let i=Ct(a.shape),o=FA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Oa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=ac({inputs:{input:a},backend:n}),o=FA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Mn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return YP(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=gb({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var JP={kernelName:Qa,backendName:"webgl",kernelFunc:FA},xb="return ceil(x);",QP=Xe({opSnippet:xb,packedOpSnippet:xb,cpuKernelImpl:gz}),eW={kernelName:es,backendName:"webgl",kernelFunc:QP},tW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},ZP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},nW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function YP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;Y().getBool("WEBGL_PACK_CLIP")?o=new ZP(a.shape):o=new KP(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var JP={kernelName:ya,backendName:"webgl",kernelFunc:YP},QP=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function rW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;Y().getBool("WEBGL_PACK_CLIP")?o=new nW(a.shape):o=new tW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var aW={kernelName:xa,backendName:"webgl",kernelFunc:rW},sW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function yb(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function eW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new QP(r.shape),i=[yb(r,a.complexTensorInfos.real),yb(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var tW={kernelName:Xl,backendName:"webgl",kernelFunc:eW},nW=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f= ${o[f-1]}) { return getChannel( - getT${f}(${tp(i,l,m)}), - vec2(${tp(u,l,m)})); + getT${f}(${np(i,l,m)}), + vec2(${np(u,l,m)})); }`}let d=o.length,p=o[o.length-1];h+=` return getChannel( - getT${d}(${tp(i,l,p)}), - vec2(${tp(u,l,p)}));`,this.userCode=` + getT${d}(${np(i,l,p)}), + vec2(${np(u,l,p)}));`,this.userCode=` float getValue(${i.map(f=>"int "+f)}) { ${h} } @@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(result); } - `}};function tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function np(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return $n({inputs:{x:a.complexTensorInfos.imag},backend:n})}var aW={kernelName:vh,backendName:"webgl",kernelFunc:np};function xl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>tc({inputs:{input:f},backend:n})),c=e.map(f=>np({inputs:{input:f},backend:n})),h=xl(u,t,n),d=xl(c,t,n),p=Ma({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=gb(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=pz(h,c,r,d),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=xl(e.slice(0,u),t,n),h=xl(e.slice(u),t,n),d=xl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new rW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=gb(e,t,n),i=new nW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=ge({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function gb(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ge({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function xb(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return $n({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),xl(o,s,n)}var sW={kernelName:ji,backendName:"webgl",kernelFunc:xb},wb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) { + `}};function np(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function rp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Mn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var cW={kernelName:kh,backendName:"webgl",kernelFunc:rp};function bl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>ac({inputs:{input:f},backend:n})),c=e.map(f=>rp({inputs:{input:f},backend:n})),h=bl(u,t,n),d=bl(c,t,n),p=Oa({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=bb(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=xz(h,c,r,d),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=bl(e.slice(0,u),t,n),h=bl(e.slice(u),t,n),d=bl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new uW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=bb(e,t,n),i=new lW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=ge({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function bb(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ge({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function _b(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Mn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),bl(o,s,n)}var hW={kernelName:qi,backendName:"webgl",kernelFunc:_b},vb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?w=`float activation(float a) { @@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${b} setOutput(result); } - `}},iW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` + `}},dW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${a}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${r}); @@ -2110,7 +2110,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},oW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=un(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=` + `}},pW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=un(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=` blockIndex = rc.y + ${_}; pos = rc.x + ${b}; @@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${m.output} = result; } - `}};function bb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>ub,w=l[2]%2!=0&&!!u.isPacked;if(g||!Y().getBool("WEBGL_LAZILY_UNPACK")||!Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=ge({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Qd({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ge({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(qu(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Qd({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=$n({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function _b({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=ge({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=ge({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new oW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=ge({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let $=a!=null,D=s!=null,L=o==="leakyrelu",P=o?Yd(o,!0):null,U=new rb(E.shape,x.shape,[1,A,n.outChannels],g,w,$,P,D,L),j=[E,x];if(a&&j.push(a),D&&j.push(s),L){let J=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(J),b.push(J)}let X=r.runWebGLProgram(U,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=ge({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let J of b)r.disposeIntermediateTensorInfo(J);return ee}function lW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=bb({x:a,filter:s,convInfo:d,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=_b({x:a,filter:s,convInfo:d,backend:n});else{let m=new wb(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=ge({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var uW={kernelName:Qa,backendName:"webgl",kernelFunc:lW},cW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function kb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>db,w=l[2]%2!=0&&!!u.isPacked;if(g||!Y().getBool("WEBGL_LAZILY_UNPACK")||!Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=ge({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=ep({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ge({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Zu(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=ep({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Mn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function Ib({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=ge({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=ge({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new pW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=ge({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",P=o?Jd(o,!0):null,U=new ib(E.shape,x.shape,[1,A,n.outChannels],g,w,M,P,D,L),H=[E,x];if(a&&H.push(a),D&&H.push(s),L){let J=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(J),b.push(J)}let X=r.runWebGLProgram(U,H,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=ge({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let J of b)r.disposeIntermediateTensorInfo(J);return ee}function fW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=kb({x:a,filter:s,convInfo:d,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=Ib({x:a,filter:s,convInfo:d,backend:n});else{let m=new vb(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=ge({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var mW={kernelName:ts,backendName:"webgl",kernelFunc:fW},AW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},hW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` + `}},yW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},dW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},gW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},pW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` + `}},xW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}};function fW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new cW(d);return n.runWebGLProgram(p,[a,s],"float32")}var mW={kernelName:hh,backendName:"webgl",kernelFunc:fW};function AW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new hW(d);return n.runWebGLProgram(p,[a,s],"float32")}var yW={kernelName:es,backendName:"webgl",kernelFunc:AW};function gW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new iW(u);return n.runWebGLProgram(c,[a,s],"float32")}var xW={kernelName:Kl,backendName:"webgl",kernelFunc:gW};function wW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new dW(u);return n.runWebGLProgram(c,[a,s],"float32")}var bW={kernelName:dh,backendName:"webgl",kernelFunc:wW};function _W(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new pW(u);return n.runWebGLProgram(c,[a,s],"float32")}var vW={kernelName:ph,backendName:"webgl",kernelFunc:_W},kW=nb+` + `}};function wW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new AW(d);return n.runWebGLProgram(p,[a,s],"float32")}var bW={kernelName:dh,backendName:"webgl",kernelFunc:wW};function _W(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new yW(d);return n.runWebGLProgram(p,[a,s],"float32")}var vW={kernelName:ns,backendName:"webgl",kernelFunc:_W};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new dW(u);return n.runWebGLProgram(c,[a,s],"float32")}var IW={kernelName:Jl,backendName:"webgl",kernelFunc:kW};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new gW(u);return n.runWebGLProgram(c,[a,s],"float32")}var SW={kernelName:ph,backendName:"webgl",kernelFunc:NW};function TW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new xW(u);return n.runWebGLProgram(c,[a,s],"float32")}var EW={kernelName:fh,backendName:"webgl",kernelFunc:TW},CW=sb+` return cos(x); -`,IW=Xe({opSnippet:kW}),NW={kernelName:ts,backendName:"webgl",kernelFunc:IW},SW=` +`,RW=Xe({opSnippet:CW}),FW={kernelName:rs,backendName:"webgl",kernelFunc:RW},MW=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,TW=Xe({opSnippet:SW}),EW={kernelName:Hi,backendName:"webgl",kernelFunc:TW},CW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` +`,$W=Xe({opSnippet:MW}),DW={kernelName:Xi,backendName:"webgl",kernelFunc:$W},OW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); const float width_ratio = float(${g}); void main() { @@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput(newValue); } } - `}},RW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new CW(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},FW={kernelName:Gi,backendName:"webgl",kernelFunc:RW},Ib=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${vb(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` + `}},zW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new OW(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},LW={kernelName:Ki,backendName:"webgl",kernelFunc:zW},Tb=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${Nb(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${lt(r)} coords = getOutputCoords(); - int end = ${kb(r,"coords")}; + int end = ${Sb(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; - ${kb(r,"coords")} = idx; - val += getX(${vb(r,"coords")}); + ${Sb(r,"coords")} = idx; + val += getX(${Nb(r,"coords")}); } setOutput(val); } - `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function vb(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kb(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function $W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=wn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=$n({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new Ib(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new Ib(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=C.getUndoAxesPermutation(u),m=wn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var MW={kernelName:ns,backendName:"webgl",kernelFunc:$W};function DW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Uw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=hz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var OW={kernelName:fh,backendName:"webgl",kernelFunc:DW},zW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function Nb(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Sb(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function PW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=wn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Mn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new Tb(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new Tb(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=C.getUndoAxesPermutation(u),m=wn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var WW={kernelName:as,backendName:"webgl",kernelFunc:PW};function BW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Gw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=yz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var VW={kernelName:mh,backendName:"webgl",kernelFunc:BW},UW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new zW(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var PW={kernelName:qi,backendName:"webgl",kernelFunc:LW},Nb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new UW(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var jW={kernelName:Zi,backendName:"webgl",kernelFunc:HW},Eb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { @@ -2503,7 +2503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${y} setOutput(result); } - `}},Sb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new Sb(h):d=new Nb(h),n.runWebGLProgram(d,[a,s],"float32")}var BW={kernelName:rs,backendName:"webgl",kernelFunc:WW},VW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function GW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new Cb(h):d=new Eb(h),n.runWebGLProgram(d,[a,s],"float32")}var qW={kernelName:ss,backendName:"webgl",kernelFunc:GW},XW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},UW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},KW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -2736,13 +2736,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}};function jW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new VW(h);return n.runWebGLProgram(d,[a,s],"float32")}var HW={kernelName:mh,backendName:"webgl",kernelFunc:jW};function GW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new UW(h);return n.runWebGLProgram(d,[a,s],"float32")}var qW={kernelName:Ah,backendName:"webgl",kernelFunc:GW},XW=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function ZW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new XW(h);return n.runWebGLProgram(d,[a,s],"float32")}var YW={kernelName:Ah,backendName:"webgl",kernelFunc:ZW};function JW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new KW(h);return n.runWebGLProgram(d,[a,s],"float32")}var QW={kernelName:yh,backendName:"webgl",kernelFunc:JW},eB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function KW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=ge({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new XW(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ge({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var ZW={kernelName:yh,backendName:"webgl",kernelFunc:KW},YW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` + `}};function tB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=ge({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new eB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ge({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var nB={kernelName:gh,backendName:"webgl",kernelFunc:tB},rB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${c}, ${h}); const float neg_infinity = -3.4e38; @@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam float result = curVal; setOutput(result); } - `}};function JW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new YW(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=ge({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var QW={kernelName:Zl,backendName:"webgl",kernelFunc:JW},eB="return (x >= 0.0) ? x : (exp(x) - 1.0);",tB=` + `}};function aB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new rB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=ge({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var sB={kernelName:Ql,backendName:"webgl",kernelFunc:aB},iB="return (x >= 0.0) ? x : (exp(x) - 1.0);",oB=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,nB=Xe({opSnippet:eB,packedOpSnippet:tB}),rB={kernelName:Xi,backendName:"webgl",kernelFunc:nB},aB="return (b >= 1.0) ? a : a * (b + 1.0);",sB=` +`,lB=Xe({opSnippet:iB,packedOpSnippet:oB}),uB={kernelName:Yi,backendName:"webgl",kernelFunc:lB},cB="return (b >= 1.0) ? a : a * (b + 1.0);",hB=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,iB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(sB,r.shape,a.shape):new gl(aB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},oB={kernelName:wh,backendName:"webgl",kernelFunc:iB},lB=` +`,dB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(hB,r.shape,a.shape):new wl(cB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},pB={kernelName:bh,backendName:"webgl",kernelFunc:dB},fB=` return vec4(equal(a, b)); -`,uB="return float(a == b);",cB=Qt({opSnippet:uB,packedOpSnippet:lB,dtype:"bool"}),hB={kernelName:Zi,backendName:"webgl",kernelFunc:cB},dB=` +`,mB="return float(a == b);",AB=Qt({opSnippet:mB,packedOpSnippet:fB,dtype:"bool"}),yB={kernelName:Qi,backendName:"webgl",kernelFunc:AB},gB=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,pB=Xe({opSnippet:dB}),fB={kernelName:Ki,backendName:"webgl",kernelFunc:pB},Tb="return exp(x);",Eb=Xe({opSnippet:Tb,packedOpSnippet:Tb,cpuKernelImpl:fz}),mB={kernelName:ss,backendName:"webgl",kernelFunc:Eb};function FA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ge({inputs:{x:s},backend:r,attrs:{shape:o}})}var AB={kernelName:Yi,backendName:"webgl",kernelFunc:FA},Cb="return exp(x) - 1.0;",yB=Xe({opSnippet:Cb,packedOpSnippet:Cb,cpuKernelImpl:mz}),gB={kernelName:Ji,backendName:"webgl",kernelFunc:yB},Rb=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,xB=Xe({opSnippet:gB}),wB={kernelName:Ji,backendName:"webgl",kernelFunc:xB},Rb="return exp(x);",Fb=Xe({opSnippet:Rb,packedOpSnippet:Rb,cpuKernelImpl:wz}),bB={kernelName:os,backendName:"webgl",kernelFunc:Fb};function MA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ge({inputs:{x:s},backend:r,attrs:{shape:o}})}var _B={kernelName:eo,backendName:"webgl",kernelFunc:MA},Mb="return exp(x) - 1.0;",vB=Xe({opSnippet:Mb,packedOpSnippet:Mb,cpuKernelImpl:bz}),kB={kernelName:to,backendName:"webgl",kernelFunc:vB},$b=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${a}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function Fb(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ge({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new Rb("real",l,t),c=new Rb("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Ma({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=ge({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function xB(e){let{inputs:t,backend:n}=e,{input:r}=t;return Fb(r,!1,n)}var wB={kernelName:bh,backendName:"webgl",kernelFunc:xB},bB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function Db(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ge({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new $b("real",l,t),c=new $b("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Oa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=ge({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function IB(e){let{inputs:t,backend:n}=e,{input:r}=t;return Db(r,!1,n)}var NB={kernelName:_h,backendName:"webgl",kernelFunc:IB},SB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } - `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function $A(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new bB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var _B={kernelName:Yl,backendName:"webgl",kernelFunc:$A},vB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function $A(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new SB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var TB={kernelName:eu,backendName:"webgl",kernelFunc:$A},EB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(outputValue); } - `}},kB={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new vB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},$b="return floor(x);",IB=Xe({opSnippet:$b,packedOpSnippet:$b,cpuKernelImpl:Az}),NB={kernelName:is,backendName:"webgl",kernelFunc:IB},SB=` + `}},CB={kernelName:no,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new EB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},Ob="return floor(x);",RB=Xe({opSnippet:Ob,packedOpSnippet:Ob,cpuKernelImpl:_z}),FB={kernelName:ls,backendName:"webgl",kernelFunc:RB},MB=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } else { return NAN; } -`,TB=` +`,$B=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,EB=Qt({opSnippet:SB,packedOpSnippet:TB,dtype:"int32"}),CB={kernelName:os,backendName:"webgl",kernelFunc:EB},RB=class{constructor(e){this.variableNames=["A"];let t=un(),[n,r]=e;this.outputShape=e,this.userCode=` +`,DB=Qt({opSnippet:MB,packedOpSnippet:$B,dtype:"int32"}),OB={kernelName:us,backendName:"webgl",kernelFunc:DB},zB=class{constructor(e){this.variableNames=["A"];let t=un(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},FB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=un(),[n,r]=e;this.outputShape=e,this.userCode=` + `}},LB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=un(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${t.output} = result; } - `}},MB={kernelName:Dh,backendName:"webgl",kernelFunc:$B},wl;function $B(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(wl==null&&(wl=document.createElement("canvas").getContext("2d")),wl.canvas.width=u,wl.canvas.height=c,wl.drawImage(a,0,0,u,c),a=wl.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Gn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let f=Y().getBool("WEBGL_PACK")?new FB(d):new RB(d),m=n.runWebGLProgram(f,[p],"int32");return n.disposeData(p.dataId),m}function DB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=bb({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=_b({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?Yd(p,!1):null,T=new wb(A,b,N,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let $=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push($),g.push($)}y=n.runWebGLProgram(T,E,"float32")}let w=ge({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var OB={kernelName:Ws,backendName:"webgl",kernelFunc:DB};function zB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?Yd(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let N;y?N=new Sb(A,b,g,_,x):N=new Nb(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var LB={kernelName:Bs,backendName:"webgl",kernelFunc:zB},PB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` + `}},WB={kernelName:Oh,backendName:"webgl",kernelFunc:PB},_l;function PB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(_l==null&&(_l=document.createElement("canvas").getContext("2d")),_l.canvas.width=u,_l.canvas.height=c,_l.drawImage(a,0,0,u,c),a=_l.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let f=Y().getBool("WEBGL_PACK")?new LB(d):new zB(d),m=n.runWebGLProgram(f,[p],"int32");return n.disposeData(p.dataId),m}function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=kb({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=Ib({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?Jd(p,!1):null,T=new vb(A,b,N,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let w=ge({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var VB={kernelName:Vs,backendName:"webgl",kernelFunc:BB};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?Jd(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let N;y?N=new Cb(A,b,g,_,x):N=new Eb(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var HB={kernelName:Us,backendName:"webgl",kernelFunc:UB},jB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${r} strides = ${r}(${this.strides}); void main() { ${a} coords = getOutputCoords(); @@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(getX(flattenIndex, coords[1])); } - `}};function WB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=ge({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=ge({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new PB(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=ge({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var BB={kernelName:to,backendName:"webgl",kernelFunc:WB},UB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=VB(e,2);this.userCode=` + `}};function GB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=ge({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=ge({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new jB(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=ge({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var qB={kernelName:ao,backendName:"webgl",kernelFunc:GB},KB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=XB(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${r})); } - `}};function VB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new UB(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=ge({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var HB={kernelName:eo,backendName:"webgl",kernelFunc:jB},GB="return float(a > b);",qB=` + `}};function XB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new KB(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=ge({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var YB={kernelName:ro,backendName:"webgl",kernelFunc:ZB},JB="return float(a > b);",QB=` return vec4(greaterThan(a, b)); -`,XB=Qt({opSnippet:GB,packedOpSnippet:qB,cpuKernelImpl:gz,dtype:"bool"}),KB={kernelName:no,backendName:"webgl",kernelFunc:XB},ZB="return float(a >= b);",YB=` +`,eV=Qt({opSnippet:JB,packedOpSnippet:QB,cpuKernelImpl:kz,dtype:"bool"}),tV={kernelName:so,backendName:"webgl",kernelFunc:eV},nV="return float(a >= b);",rV=` return vec4(greaterThanEqual(a, b)); -`,JB=Qt({opSnippet:ZB,packedOpSnippet:YB,dtype:"bool"}),QB={kernelName:us,backendName:"webgl",kernelFunc:JB};function eV(e){let{inputs:t,backend:n}=e,{input:r}=t;return Fb(r,!0,n)}var tV={kernelName:_h,backendName:"webgl",kernelFunc:eV},nV="return float(!isnan(x) && !isinf(x));",rV=Xe({opSnippet:nV,dtype:"bool"}),aV={kernelName:ro,backendName:"webgl",kernelFunc:rV},sV="return float(isinf(x));",iV=Xe({opSnippet:sV,dtype:"bool"}),oV={kernelName:ao,backendName:"webgl",kernelFunc:iV},lV="return float(isnan(x));",uV=Xe({opSnippet:lV,dtype:"bool"}),cV={kernelName:so,backendName:"webgl",kernelFunc:uV},hV="return float(a < b);",dV=` +`,aV=Qt({opSnippet:nV,packedOpSnippet:rV,dtype:"bool"}),sV={kernelName:hs,backendName:"webgl",kernelFunc:aV};function iV(e){let{inputs:t,backend:n}=e,{input:r}=t;return Db(r,!0,n)}var oV={kernelName:vh,backendName:"webgl",kernelFunc:iV},lV="return float(!isnan(x) && !isinf(x));",uV=Xe({opSnippet:lV,dtype:"bool"}),cV={kernelName:io,backendName:"webgl",kernelFunc:uV},hV="return float(isinf(x));",dV=Xe({opSnippet:hV,dtype:"bool"}),pV={kernelName:oo,backendName:"webgl",kernelFunc:dV},fV="return float(isnan(x));",mV=Xe({opSnippet:fV,dtype:"bool"}),AV={kernelName:lo,backendName:"webgl",kernelFunc:mV},yV="return float(a < b);",gV=` return vec4(lessThan(a, b)); -`,pV=Qt({opSnippet:hV,packedOpSnippet:dV,cpuKernelImpl:xz,dtype:"bool"}),fV={kernelName:io,backendName:"webgl",kernelFunc:pV},mV="return float(a <= b);",AV=` +`,xV=Qt({opSnippet:yV,packedOpSnippet:gV,cpuKernelImpl:Iz,dtype:"bool"}),wV={kernelName:uo,backendName:"webgl",kernelFunc:xV},bV="return float(a <= b);",_V=` return vec4(lessThanEqual(a, b)); -`,yV=Qt({opSnippet:mV,packedOpSnippet:AV,dtype:"bool"}),gV={kernelName:oo,backendName:"webgl",kernelFunc:yV};function xV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=wz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var wV={kernelName:kh,backendName:"webgl",kernelFunc:xV},bV=`if (x < 0.0) return NAN; - return log(x);`,_V=` +`,vV=Qt({opSnippet:bV,packedOpSnippet:_V,dtype:"bool"}),kV={kernelName:co,backendName:"webgl",kernelFunc:vV};function IV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Nz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var NV={kernelName:Ih,backendName:"webgl",kernelFunc:IV},SV=`if (x < 0.0) return NAN; + return log(x);`,TV=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; @@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam result.a = isNaN.a == 1.0 ? NAN : result.a; return result; -`,vV=Xe({opSnippet:bV,packedOpSnippet:_V,cpuKernelImpl:bz}),kV={kernelName:ds,backendName:"webgl",kernelFunc:vV},IV="return log(1.0 + x);",NV=Xe({opSnippet:IV}),SV={kernelName:lo,backendName:"webgl",kernelFunc:NV},TV="return float(a >= 1.0 && b >= 1.0);",EV=` +`,EV=Xe({opSnippet:SV,packedOpSnippet:TV,cpuKernelImpl:Sz}),CV={kernelName:fs,backendName:"webgl",kernelFunc:EV},RV="return log(1.0 + x);",FV=Xe({opSnippet:RV}),MV={kernelName:ho,backendName:"webgl",kernelFunc:FV},$V="return float(a >= 1.0 && b >= 1.0);",DV=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,CV=Qt({opSnippet:TV,packedOpSnippet:EV,dtype:"bool"}),RV={kernelName:uo,backendName:"webgl",kernelFunc:CV},FV="return float(!(x >= 1.0));",$V=Xe({opSnippet:FV}),MV={kernelName:Jl,backendName:"webgl",kernelFunc:$V},DV="return float(a >= 1.0 || b >= 1.0);",OV=` +`,OV=Qt({opSnippet:$V,packedOpSnippet:DV,dtype:"bool"}),zV={kernelName:po,backendName:"webgl",kernelFunc:OV},LV="return float(!(x >= 1.0));",PV=Xe({opSnippet:LV}),WV={kernelName:tu,backendName:"webgl",kernelFunc:PV},BV="return float(a >= 1.0 || b >= 1.0);",VV=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,zV=Qt({opSnippet:DV,packedOpSnippet:OV,dtype:"bool"}),LV={kernelName:Ql,backendName:"webgl",kernelFunc:zV},PV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` +`,UV=Qt({opSnippet:BV,packedOpSnippet:VV,dtype:"bool"}),HV={kernelName:nu,backendName:"webgl",kernelFunc:UV},jV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam float val = x * ${o}; setOutput(val); } - `}},WV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` + `}},GV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},BV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new WV(a.shape,s,i,o,l):new PV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},VV={kernelName:eu,backendName:"webgl",kernelFunc:BV},UV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` + `}},qV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new GV(a.shape,s,i,o,l):new jV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},XV={kernelName:ru,backendName:"webgl",kernelFunc:qV},KV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(result); } - `}},jV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new UV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},HV={kernelName:Ih,backendName:"webgl",kernelFunc:jV};function GV(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ge({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ui(i,e.dtype,"max",r),l=ge({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function Mb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new KV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},YV={kernelName:Nh,backendName:"webgl",kernelFunc:ZV};function JV(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ge({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=di(i,e.dtype,"max",r),l=ge({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function zb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return $n({inputs:{x:a},backend:n});let h=new Qu(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var QV={kernelName:ms,backendName:"webgl",kernelFunc:JV};function eU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new EA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var tU={kernelName:tu,backendName:"webgl",kernelFunc:eU},nU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` +`,nU=Qt({opSnippet:eU,packedOpSnippet:tU,cpuKernelImpl:Ez}),rU={kernelName:As,backendName:"webgl",kernelFunc:nU};function aU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;pl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Mn({inputs:{x:a},backend:n});let h=new nc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var sU={kernelName:ys,backendName:"webgl",kernelFunc:aU};function iU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new CA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var oU={kernelName:au,backendName:"webgl",kernelFunc:iU},lU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}},rU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` + `}},uU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${h}, ${d}); void main() { @@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam } setOutput(dotProd); } - `}};function aU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new EA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new rU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var sU={kernelName:Sh,backendName:"webgl",kernelFunc:aU};function iU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;hl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new Qu(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new nU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var oU={kernelName:Nh,backendName:"webgl",kernelFunc:iU};function lU(e,t,n,r){let a=new Qu(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new Qu(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var uU={kernelName:Th,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${i}; @@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam ${a} coords = outC - start; setOutput(getX(${o})); } - `}},xU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` + `}},IU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` ${a} source = rc; if (source < start) { source = start * 2 - source - ${h}; 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void main() { @@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fP=Xe({opSnippet:pP}),mP={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},SU=` + `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},MU=` if (a == b) { return 1.0; }; -return a / b;`,TU=` +return a / b;`,$U=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3387,14 +3387,14 @@ return a / b;`,TU=` } return result; -`,Db=Qt({opSnippet:SU,packedOpSnippet:TU,checkOutOfBounds:!0}),EU={kernelName:as,backendName:"webgl",kernelFunc:Db},Ob="return a - b;",zb=Qt({opSnippet:Ob,packedOpSnippet:Ob,supportsComplex:!0,cpuKernelImpl:Fz}),CU={kernelName:Os,backendName:"webgl",kernelFunc:zb};function Lb(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=Mb({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=ge({inputs:{x:o},backend:n,attrs:{shape:l}}),c=zb({inputs:{a,b:u},backend:n}),h=Eb({inputs:{x:c},backend:n}),d=TA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=ge({inputs:{x:d},backend:n,attrs:{shape:l}}),f=Db({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var RU={kernelName:Ms,backendName:"webgl",kernelFunc:Lb};function FU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:Lb({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new NU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var $U={kernelName:Eh,backendName:"webgl",kernelFunc:FU},Pb="return -x;";function MU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Nz(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,Pb):a=new $a(r.shape,Pb),n.runWebGLProgram(a,[r],r.dtype)}var DU={kernelName:ho,backendName:"webgl",kernelFunc:MU},OU=Rr.nonMaxSuppressionV3Impl;function zU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=OU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var LU={kernelName:fo,backendName:"webgl",kernelFunc:zU},PU=Rr.nonMaxSuppressionV4Impl;function WU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=PU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var BU={kernelName:mo,backendName:"webgl",kernelFunc:WU},VU=Rr.nonMaxSuppressionV5Impl;function UU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=VU(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var jU={kernelName:Ao,backendName:"webgl",kernelFunc:UU},HU=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`,Lb=Qt({opSnippet:MU,packedOpSnippet:$U,checkOutOfBounds:!0}),DU={kernelName:is,backendName:"webgl",kernelFunc:Lb},Pb="return a - b;",Wb=Qt({opSnippet:Pb,packedOpSnippet:Pb,supportsComplex:!0,cpuKernelImpl:Lz}),OU={kernelName:Ls,backendName:"webgl",kernelFunc:Wb};function Bb(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=zb({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=ge({inputs:{x:o},backend:n,attrs:{shape:l}}),c=Wb({inputs:{a,b:u},backend:n}),h=Fb({inputs:{x:c},backend:n}),d=EA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=ge({inputs:{x:d},backend:n,attrs:{shape:l}}),f=Lb({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var zU={kernelName:Os,backendName:"webgl",kernelFunc:Bb};function LU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:Bb({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new FU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var PU={kernelName:Ch,backendName:"webgl",kernelFunc:LU},Vb="return -x;";function WU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Fz(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new xl(r.shape,Vb):a=new Da(r.shape,Vb),n.runWebGLProgram(a,[r],r.dtype)}var BU={kernelName:mo,backendName:"webgl",kernelFunc:WU},VU=Fr.nonMaxSuppressionV3Impl;function UU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=VU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var HU={kernelName:yo,backendName:"webgl",kernelFunc:UU},jU=Fr.nonMaxSuppressionV4Impl;function GU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=jU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var qU={kernelName:go,backendName:"webgl",kernelFunc:GU},XU=Fr.nonMaxSuppressionV5Impl;function KU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=XU(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ZU={kernelName:xo,backendName:"webgl",kernelFunc:KU},YU=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } - `}},GU=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new HU(l,s,i,o),c=ge({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=ge({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},qU={kernelName:ws,backendName:"webgl",kernelFunc:GU};function rp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=tc({inputs:{input:r},backend:n}),s=rp({inputs:{x:a},backend:n}),i=np({inputs:{input:r},backend:n}),o=rp({inputs:{x:i},backend:n}),l=Ma({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $A({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var XU={kernelName:Mo,backendName:"webgl",kernelFunc:rp};function Wb(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=tc({inputs:{input:r},backend:n}),s=Wb({inputs:{x:a},backend:n}),i=np({inputs:{input:r},backend:n}),o=rp({inputs:{x:i},backend:n}),l=Ma({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $A({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var KU={kernelName:yo,backendName:"webgl",kernelFunc:Wb};function ZU(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return FA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=FA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=xb({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var YU={kernelName:go,backendName:"webgl",kernelFunc:ZU},JU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` + `}},JU=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new YU(l,s,i,o),c=ge({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=ge({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},QU={kernelName:_s,backendName:"webgl",kernelFunc:JU};function ap(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=ac({inputs:{input:r},backend:n}),s=ap({inputs:{x:a},backend:n}),i=rp({inputs:{input:r},backend:n}),o=ap({inputs:{x:i},backend:n}),l=Oa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $A({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var eH={kernelName:zo,backendName:"webgl",kernelFunc:ap};function Ub(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=ac({inputs:{input:r},backend:n}),s=Ub({inputs:{x:a},backend:n}),i=rp({inputs:{input:r},backend:n}),o=ap({inputs:{x:i},backend:n}),l=Oa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $A({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var tH={kernelName:wo,backendName:"webgl",kernelFunc:Ub};function nH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return MA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=MA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=_b({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var rH={kernelName:bo,backendName:"webgl",kernelFunc:nH},aH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3419,7 +3419,7 @@ return a / b;`,TU=` setOutput(getX(${o})); } } - `}},QU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; + `}},sH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${u}) { `,r===1?"":`} rc = outputLoc; @@ -3443,7 +3443,7 @@ return a / b;`,TU=` ${p} setOutput(result); } - `}},Bb=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QU(a.shape,s,i):new JU(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},ej={kernelName:bs,backendName:"webgl",kernelFunc:Bb},tj=` + `}},Hb=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sH(a.shape,s,i):new aH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},iH={kernelName:vs,backendName:"webgl",kernelFunc:Hb},oH=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -3452,7 +3452,7 @@ return a / b;`,TU=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,nj=` +`,lH=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -3466,11 +3466,11 @@ return a / b;`,TU=` result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); - `+Zd+` + `+Yd+` return result; -`,rj=Qt({opSnippet:tj,packedOpSnippet:nj}),aj={kernelName:_s,backendName:"webgl",kernelFunc:rj};function sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=wn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=Sz(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=zh(a.dtype),w=ui(y,g,"prod",n);p=ge({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,u);p=ge({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var ij={kernelName:xo,backendName:"webgl",kernelFunc:sj},Vb=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Tz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},oj={kernelName:ru,backendName:"webgl",kernelFunc:Vb},lj="return 1.0 / x;",uj=Xe({opSnippet:lj}),cj={kernelName:wo,backendName:"webgl",kernelFunc:uj},hj=mr+` +`,uH=Qt({opSnippet:oH,packedOpSnippet:lH}),cH={kernelName:ks,backendName:"webgl",kernelFunc:uH};function hH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=wn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=Mz(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Lh(a.dtype),w=di(y,g,"prod",n);p=ge({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,u);p=ge({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var dH={kernelName:_o,backendName:"webgl",kernelFunc:hH},jb=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=$z(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},pH={kernelName:iu,backendName:"webgl",kernelFunc:jb},fH="return 1.0 / x;",mH=Xe({opSnippet:fH}),AH={kernelName:vo,backendName:"webgl",kernelFunc:mH},yH=yr+` return (x < 0.0) ? 0.0 : x; -`,dj=` +`,gH=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3480,9 +3480,9 @@ return a / b;`,TU=` result.a = isNaN.a ? x.a : result.a; return result; -`,pj=Xe({opSnippet:hj,packedOpSnippet:dj}),fj={kernelName:ks,backendName:"webgl",kernelFunc:pj},mj=mr+` +`,xH=Xe({opSnippet:yH,packedOpSnippet:gH}),wH={kernelName:Ns,backendName:"webgl",kernelFunc:xH},bH=yr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Aj=` +`,_H=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3492,7 +3492,7 @@ return a / b;`,TU=` result.a = isNaN.a ? x.a : result.a; return result; -`,yj=Xe({opSnippet:mj,packedOpSnippet:Aj}),gj={kernelName:Ns,backendName:"webgl",kernelFunc:yj},xj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,vH=Xe({opSnippet:bH,packedOpSnippet:_H}),kH={kernelName:Ts,backendName:"webgl",kernelFunc:vH},IH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3525,7 +3525,7 @@ return a / b;`,TU=` setOutput(newValue); } - `}},wj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},NH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, @@ -3602,7 +3602,7 @@ return a / b;`,TU=` setOutput(newValue); } - `}};function bj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new wj(a.shape,l,u,s,i):new xj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var _j={kernelName:Is,backendName:"webgl",kernelFunc:bj},vj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new NH(a.shape,l,u,s,i):new IH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var TH={kernelName:Ss,backendName:"webgl",kernelFunc:SH},EH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3683,7 +3683,7 @@ return a / b;`,TU=` setOutput(accumulator); } - `}};function kj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new vj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Ij={kernelName:Fh,backendName:"webgl",kernelFunc:kj},Nj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new EH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var RH={kernelName:Mh,backendName:"webgl",kernelFunc:CH},FH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3705,7 +3705,7 @@ return a / b;`,TU=` setOutput(newValue); } - `}};function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new Nj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var Tj={kernelName:au,backendName:"webgl",kernelFunc:Sj},Ej=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function MH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new FH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var $H={kernelName:ou,backendName:"webgl",kernelFunc:MH},DH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3775,7 +3775,7 @@ return a / b;`,TU=` setOutput(accumulator); } - `}};function Cj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Ej(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Rj={kernelName:Rh,backendName:"webgl",kernelFunc:Cj},Fj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function OH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new DH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var zH={kernelName:Fh,backendName:"webgl",kernelFunc:OH},LH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -3785,7 +3785,7 @@ return a / b;`,TU=` ${s} coords = getOutputCoords(); setOutput(getX(${a})); } - `}},$j=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=cn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=` + `}},PH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=cn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -3813,7 +3813,7 @@ return a / b;`,TU=` } setOutput(result); } - `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return $n({inputs:{x:a},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $j(a.shape,o):new Fj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Dj={kernelName:Ss,backendName:"webgl",kernelFunc:Mj},Oj=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` + `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function WH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Mn({inputs:{x:a},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PH(a.shape,o):new LH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var BH={kernelName:Es,backendName:"webgl",kernelFunc:WH},VH=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` vec3 fill = vec3(${n.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -3830,7 +3830,7 @@ return a / b;`,TU=` } setOutput(outputValue); } - `}},zj={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Oj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},Lj=` + `}},UH={kernelName:Lo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new VH(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},HH=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -3845,7 +3845,7 @@ return a / b;`,TU=` return base + 1.0; } } -`,Pj=Xe({opSnippet:Lj}),Wj={kernelName:Ts,backendName:"webgl",kernelFunc:Pj},Bj="return inversesqrt(x);",Vj=Xe({opSnippet:Bj,cpuKernelImpl:Ez}),Uj={kernelName:Es,backendName:"webgl",kernelFunc:Vj},Ub=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` +`,jH=Xe({opSnippet:HH}),GH={kernelName:Cs,backendName:"webgl",kernelFunc:jH},qH="return inversesqrt(x);",XH=Xe({opSnippet:qH,cpuKernelImpl:Dz}),KH={kernelName:Rs,backendName:"webgl",kernelFunc:XH},Gb=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${a}); void main() { @@ -3865,7 +3865,7 @@ return a / b;`,TU=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function jj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=C.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=ge({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=ge({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new Ub(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=ge({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var Hj={kernelName:_o,backendName:"webgl",kernelFunc:jj},Gj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Zj=Xe({opSnippet:Kj}),Yj={kernelName:ko,backendName:"webgl",kernelFunc:Zj},Jj="return 1.0 / (1.0 + exp(-1.0 * x));",Qj=Xe({opSnippet:Jj}),eH={kernelName:Rs,backendName:"webgl",kernelFunc:Qj},tH=` +`,nj=Xe({opSnippet:tj}),rj={kernelName:So,backendName:"webgl",kernelFunc:nj},aj="return 1.0 / (1.0 + exp(-1.0 * x));",sj=Xe({opSnippet:aj}),ij={kernelName:Ms,backendName:"webgl",kernelFunc:sj},oj=` if (isnan(x)) { return 0.0; } return sign(x); -`,nH=Xe({opSnippet:tH}),rH={kernelName:So,backendName:"webgl",kernelFunc:nH},aH=nb+` +`,lj=Xe({opSnippet:oj}),uj={kernelName:Co,backendName:"webgl",kernelFunc:lj},cj=sb+` return sin(x); -`,sH=Xe({opSnippet:aH}),iH={kernelName:Cs,backendName:"webgl",kernelFunc:sH},oH=` +`,hj=Xe({opSnippet:cj}),dj={kernelName:Fs,backendName:"webgl",kernelFunc:hj},pj=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,lH=Xe({opSnippet:oH}),uH={kernelName:No,backendName:"webgl",kernelFunc:lH},cH=` +`,fj=Xe({opSnippet:pj}),mj={kernelName:Eo,backendName:"webgl",kernelFunc:fj},Aj=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -3909,9 +3909,9 @@ return a / b;`,TU=` result = log(exp_x + 1.0); } return result; -`,hH=Xe({opSnippet:cH}),dH={kernelName:To,backendName:"webgl",kernelFunc:hH},pH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),A},fH={kernelName:su,backendName:"webgl",kernelFunc:pH};function mH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return s_(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,w,$,b,_,x,N,T,E,L),D}var HG={kernelName:rs,backendName:"wasm",setupFunc:UG,kernelFunc:jG},GG=!1,qG=hn(Zi,GG,"bool"),XG=bn(ss);function DA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ar({inputs:{x:a},backend:r,attrs:{shape:o}})}var KG={kernelName:Yi,backendName:"wasm",kernelFunc:DA};function ZG(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var YG={kernelName:Yl,backendName:"wasm",kernelFunc:ZG},i_;function JG(e){i_=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function QG(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return i_(s,o,l,u,c,i),a}var eq={kernelName:Qi,backendName:"wasm",kernelFunc:QG,setupFunc:JG},tq=bn(is),nq=!1,rq=hn(os,nq),o_;function aq(e){o_=e.wasm.cwrap(ls,null,["number","number","number","number","number","number","number"])}function sq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return o_(c,h,d,p,f,a,A),m}var iq={kernelName:ls,backendName:"wasm",setupFunc:aq,kernelFunc:sq},l_;function oq(e){l_=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=nc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,w=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);b=Q.id}let _=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,$=m.padInfo.left,D=m.dilationHeight,L=m.dilationWidth,P=m.strideHeight,U=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return l_(y,G,ee,J,g,_,x,b,N,T,E,$,X,D,L,P,U,j,w,A,le,f||0,te),se}var uq={kernelName:Ws,backendName:"wasm",setupFunc:oq,kernelFunc:lq},u_;function cq(e){u_=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=nc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,w=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);b=Q.id}let _=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,$=m.padInfo.left,D=m.dilationHeight,L=m.dilationWidth,P=m.strideHeight,U=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return u_(y,G,ee,J,g,_,x,b,N,T,E,$,X,D,L,P,U,j,w,A,le,f||0,te),se}var dq={kernelName:Bs,backendName:"wasm",setupFunc:cq,kernelFunc:hq},c_;function pq(e){c_=e.wasm.cwrap(to,null,["number","number","number","number","number","number","array","number"])}function fq(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Nf.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return c_(d,Mn[r.dtype],p,i,h,o,f,m),u}var mq={kernelName:to,backendName:"wasm",setupFunc:pq,kernelFunc:fq},h_;function Aq(e){h_=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function yq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=Ar({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=Ar({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],f=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return f;let m=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return h_(A,Mn[a.dtype],w,m,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),f.shape=u.outputShape,f}var gq={kernelName:eo,backendName:"wasm",setupFunc:Aq,kernelFunc:yq},xq=!1,wq=hn(no,xq,"bool"),bq=!1,_q=hn(us,bq,"bool"),d_;function vq(e){d_=e.wasm.cwrap(hs,null,["number","number","number"])}function kq(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;d_(a,n,i)}return s}var Iq={kernelName:hs,backendName:"wasm",setupFunc:vq,kernelFunc:kq},Nq=!1,Sq=hn(io,Nq,"bool"),Tq=!1,Eq=hn(oo,Tq,"bool"),Cq=bn(ds),Rq=!1,Fq=hn(uo,Rq,"bool"),p_;function $q(e){p_=e.wasm.cwrap(ps,null,["number, number, number"])}function Mq(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=bl(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",c,p);let[f,m]=C.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;p_(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var Dq={kernelName:ps,backendName:"wasm",setupFunc:$q,kernelFunc:Mq},Oq=!1,zq=hn(fs,Oq),f_;function Lq(e){f_=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pq(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=C.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,w=c.strideHeight,b=c.strideWidth,_=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(c.outShape,"float32"),T=r.dataIdMap.get(N.dataId).id;return f_(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,w,b,_,x,T),N}var Wq={kernelName:ms,backendName:"wasm",setupFunc:Lq,kernelFunc:Pq},m_;function Bq(e){m_=e.wasm.cwrap(As,null,["number, number, number"])}function Vq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=bl(i,a,t),f=h;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==o&&(u=c,l=b,f=C.getInnerMostAxes(f.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,A]=C.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=u;u.dtype!=="float32"&&(g=ip({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let w=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(w.dataId).id;m_(l,y,b)}if(p&&t.disposeData(c.dataId),s){let 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Please use 'NHWC'.`);let ie=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ie.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return h_(y,G,ee,J,g,_,x,b,N,T,E,M,X,D,L,P,U,H,w,A,le,f||0,te),ie}var mq={kernelName:Vs,backendName:"wasm",setupFunc:pq,kernelFunc:fq},d_;function Aq(e){d_=e.wasm.cwrap(Us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=sc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,w=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);b=Q.id}let _=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,D=m.dilationHeight,L=m.dilationWidth,P=m.strideHeight,U=m.strideWidth,H=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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b=C.expandShapeToKeepDim(w.shape,d);w.shape=b}return u.dtype!=="float32"&&t.disposeData(g.dataId),w}var Kq={kernelName:gs,backendName:"wasm",setupFunc:qq,kernelFunc:Xq},x_;function Zq(e){x_=e.wasm.cwrap(xs,null,["number, number, number"])}function Yq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=vl(i,a,t);if(p){let w=t.dataIdMap.get(c.dataId).id;w!==o&&(u=c,l=w)}let f=u.shape.length;C.assertAxesAreInnerMostDims("min",h,f);let[m,A]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;x_(l,y,w)}if(p&&t.disposeData(c.dataId),s){let w=C.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var Jq={kernelName:xs,backendName:"wasm",setupFunc:Zq,kernelFunc:Yq},Qq=!1,eX=hn(ws,Qq),tX=!0,nX=hn(bs,tX),rX=bn(mo);function zA(e,t){let n=new 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Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Vee(r.weightData,r.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(u),Re(c.map(h=>h.tensor))}return o}function Vee(e,t){let n=yn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Ko=class extends Qr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:wp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ko||e instanceof Qr,n;if(t){if(n=e,n.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Ko))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=wr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=yt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=gt(e.alphaRegularizer),this.alphaConstraint=Wt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ut(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(Tt(t),t==="channelsFirst"?rt(e,[0,2,3,1]):e))}function n7(e,t){return V(()=>(Tt(t),t==="channelsFirst"?rt(e,[0,2,3,4,1]):e))}function Hee(e,t,n,r=1,a="valid",s,i=1){return V(()=>{if(s==null&&(s=yr()),Tt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=rt(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=jh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Br(o,n)),o})}function r7(e,t,n,r=[1,1],a="valid",s,i,o=null){return V(()=>{if(s==null&&(s=yr()),Tt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Fy(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ia.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=rt(l,[0,3,1,2])),l})}function Gee(e,t,n,r=[1,1,1],a="valid",s,i){return V(()=>{if(s==null&&(s=yr()),Tt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=n7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Pf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Br(o,n)),s==="channelsFirst"&&(o=rt(o,[0,4,1,2,3])),o})}var $y=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",$y.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Sl(t.kernelSize,e,"kernelSize"),this.strides=Sl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=Wa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Sl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Pr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!VA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Pa(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},xc=class extends $y{constructor(e,t){super(e,t);this.kernel=null,xc.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Le(e);let n,r=this.bias==null?null:this.bias.read(),a=K_(this.activation.getClassName());if(a!=null&&this.rank===2)n=r7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Hee(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=r7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Gee(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ut(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},wc=class extends xc{constructor(e){super(2,e);wc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!VA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};wc.className="Conv2D";re.registerClass(wc);var $p=class extends xc{constructor(e){super(3,e);$p.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};$p.className="Conv3D";re.registerClass($p);var My=class extends wc{constructor(e){super(e);if(this.inputSpec=[new Ut({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ut({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Fp(o,h,u,this.padding),f=Fp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=rt(n,[0,2,3,1]));let A=Hh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=rt(A,[0,3,1,2])),this.bias!=null&&(A=Br(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ut(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Fp(t[r],o,s,this.padding),t[a]=Fp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};My.className="Conv2DTranspose";re.registerClass(My);var a7=class extends xc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=ut(e),e.length{e=Le(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=rt(e,[0,2,3,1])),n=Qf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Br(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=rt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseConstraint),e.pointwiseConstraint=Pt(this.pointwiseConstraint),e}};a7.className="SeparableConv";var Dy=class extends a7{constructor(e){super(2,e)}};Dy.className="SeparableConv2D";re.registerClass(Dy);var Mp=class extends xc{constructor(e){super(1,e);Mp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!VA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Mp.className="Conv1D";re.registerClass(Mp);var Oy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=cp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return cp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=cp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return cp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Cropping2D";re.registerClass(Oy);var zy=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,yJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Le(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=rt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return rt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};zy.className="UpSampling2D";re.registerClass(zy);function qee(e,t,n=[1,1],r="valid",a,s){return V(()=>{a==null&&(a=yr()),Tt(a);let i=Fy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Wo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}var Ly=class extends $y{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=ut(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Le(e);let n=qee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Br(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=br(t,this.kernelSize[0],this.padding,this.strides[0]),s=br(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseRegularizer),e}};Ly.className="DepthwiseConv2D";re.registerClass(Ly);function s7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function i7(e,t,n,r=!1,a,s,i=!1,o=!1){return V(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(xr(2,l));if(t=rt(t,u),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Sn(a,-1)),a=rt(a,u)),r&&(t=Cn(t,0),a!=null&&(a=Cn(a,0)));let c=[],h,d=n,p=t.shape[0],f=nr(t),m;a!=null&&(m=nr(a));for(let y=0;ye(g,d));if(a==null)h=w[0],d=w[1];else{let b=V(()=>{let _=m[y],x=En(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,$)=>w[1][$].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Rn(c,1)),[h,A,d]})}var Fr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Dp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ut({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ly(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ut({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let r=0;rVt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=s7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ut({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof fr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Le(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=i7((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return V(()=>{let t=Ct(e.shape);return t=Ee(t,[1,2]),t=cc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?XA(t,[1,n]):t):this.cell.stateSize>1?[XA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=wr(r,n);return new e(Object.assign(t,{cell:a}))}};Fr.className="RNN";re.registerClass(Fr);var pc=class extends qe{},Op=class extends pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Wa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=vl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0En(e),rate:this.dropout,training:r})),0En(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Wr(W(e,s),this.kernel.read()):a=Wr(e,this.kernel.read()),this.bias!=null&&(a=Br(a,this.bias.read())),i!=null&&(n=W(n,i));let o=oe(a,Wr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Pa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Op.className="SimpleRNNCell";re.registerClass(Op);var Py=class extends Fr{constructor(e){e.cell=new Op(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Py.className="SimpleRNN";re.registerClass(Py);var zp=class extends pc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=Wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=vl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0En(e),rate:this.dropout,training:n,count:3})),0En(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Wy.className="GRU";re.registerClass(Wy);var bc=class extends pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=vl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ut(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),u=new dp().apply([s]),c=a.apply([s*2]);return a3(a3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0En(e),rate:this.dropout,training:n,count:4})),0En(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};By.className="LSTM";re.registerClass(By);var Dp=class extends pc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{pi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(wr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return uy(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;si3(t(),n),i=()=>dc(s,t,r);return!a||a<=1?Vt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Vt(o.clone()))}var Xee=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;sVt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=br(l,r[0],a,s[0],i[0]),h=br(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};o7.className="ConvRNN2D";var Lp=class extends bc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=Sl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=Sl(r||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=a||"valid",qn(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=Sl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=ut(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends or{apply(c,h){let d=l.apply([u]),p=Er([u]),f=l.apply([u*2]);return ZA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0En(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,te)=>!se||!se[te]?J:W(se[te],J),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0En(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=on(this.kernel.read(),i,g),[N,T,E,$]=this.useBias?on(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,$,this.padding);let[D,L,P,U]=on(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,D),m=this.recurrentConv(m,L),A=this.recurrentConv(A,P),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(oe(u,f)),X=this.recurrentActivation.apply(oe(c,m)),G=oe(W(X,s),W(j,this.activation.apply(oe(h,A)))),ee=W(this.recurrentActivation.apply(oe(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Xee(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Kr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Br(a,n,this.dataFormat):a}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Lp.className="ConvLSTM2DCell";re.registerClass(Lp);var Vy=class extends o7{constructor(e){let t=new Lp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Vy.className="ConvLSTM2D";re.registerClass(Vy);var Pp=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=Le(e);if(0i3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Pp.className="Dropout";re.registerClass(Pp);var Uy=class extends Pp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Uy.className="SpatialDropout1D";re.registerClass(Uy);var jy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Gt(this.units,"units"),this.activation=Wa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ut(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ut(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),r=K_(this.activation.getClassName()),a;return r!=null?a=Wr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Wr(n,this.kernel.read()),this.bias!=null&&(a=Br(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Pa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};jy.className="Dense";re.registerClass(jy);var Hy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ut(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Oa(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:Pa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="Activation";re.registerClass(Gy);var qy=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Le(e),bJ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};qy.className="RepeatVector";re.registerClass(qy);var Xy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Le(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Reshape";re.registerClass(Xy);var Ky=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=xr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ut({ndim:this.dims.length+1})]}computeOutputShape(e){e=ut(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return rt(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Permute";re.registerClass(Ky);var Zy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Le(e),r=-1;return du(Hs(n,this.maskValue),r)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),r=-1,a=!0,s=du(Hs(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Zy.className="Masking";re.registerClass(Zy);var Yy=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(pt(e.inputLength))}this.inputDim=e.inputDim,Gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Gt(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Le(e),Hs(e,je(e))):null)}computeOutputShape(e){if(e=ut(e),this.inputLength==null)return[...e,this.outputDim];let t=pt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r{this.invokeCallHook(e,t);let n=Le(e);return n.dtype!=="int32"&&(n=uc(n,"int32")),s3(this.embeddings.read(),n.as1D()).reshape(ut(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Pt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="Embedding";re.registerClass(Yy);var gi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Da(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=za(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=xr(1,l).concat([0]);n.push(rt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=rt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(xr(0,i-1));s=rt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Sn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return V(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>_c(a,e[s].shape.length)):r=[_c(this.axes,t.shape.length),_c(this.axes,n.shape.length)],this.normalize&&(t=kp(t,r[0]),n=kp(n,r[1])),Kee(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[_c(this.axes,e.length),_c(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ag.className="Dot";re.registerClass(ag);var sg=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return dc(()=>hp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};sg.className="GaussianNoise";re.registerClass(sg);var ig=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?dc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(hp(n.shape,1,r))},()=>n,t.training||!1):n})}};ig.className="GaussianDropout";re.registerClass(ig);var og=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return dc(()=>{let r=Le(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=va(Go(n),this.rate);o=uc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Le(e),t.training||!1)}return e})}};og.className="AlphaDropout";re.registerClass(og);function vc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=H2(e,t,n,r,a,s);else if(e.rank===3)i=G2(e,t,n,r,a,s);else if(e.rank===4)i=q2(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Zee(e,t,n,r,a=.001){return V(()=>{let s=Qh(e,r),i=s.mean,o=s.variance;return[vc(e,i,o,n,t,a),i,o]})}function Yee(e,t,n,r,a=.001){return V(()=>{let s=Qh(e,r),i=s.mean,o=s.variance,l=[];for(let p of xr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[vc(e,u,c,d,h,a),i,o]})}function Jee(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),xr(0,e.rank-1))?Zee(e,t,n,r,a):Yee(e,t,n,r,a)}var lg=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=ut(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ut({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,r=Le(e),a=r.shape,s=a.length,i=xr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,xr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return vc(r,A,y,g,w,this.epsilon)}else return vc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=Jee(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{V(()=>{let w=1-g,b=A.read(),_=b.sub(y).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Pt(this.betaConstraint),gammaConstraint:Pt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};lg.className="BatchNormalization";re.registerClass(lg);var ug=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ut(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Da(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Le(e),r=n.shape,a=r.length;return V(()=>{let s=!0,{mean:i,variance:o}=Qh(n,this.axis,s),l=ci(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=yr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=bu(e,t,n,o):i=fu(e,t,n,o),a==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}function l7(e,t,n,r,a,s){return V(()=>{Tt(a),J_(s),qn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=yr()),s==null&&(s="max"),e=n7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Xf(e,t,n,o):i=zf(e,t,n,o),a==="channelsFirst"&&(i=rt(i,[0,4,1,2,3])),i})}var u7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Gt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,qn(this.padding),this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){e=ut(e);let t=br(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=cc(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ka(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},hg=class extends u7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),Wp(e,t,n,r,a,"max")}};hg.className="MaxPooling1D";re.registerClass(hg);var dg=class extends u7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),Wp(e,t,n,r,a,"avg")}};dg.className="AveragePooling1D";re.registerClass(dg);var c7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),qn(this.padding),this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},pg=class extends c7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),Wp(e,t,n,r,a,"max")}};pg.className="MaxPooling2D";re.registerClass(pg);var fg=class extends c7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),Wp(e,t,n,r,a,"avg")}};fg.className="AveragePooling2D";re.registerClass(fg);var h7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),qn(this.padding),this.inputSpec=[new Ut({ndim:5})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(n,this.poolSize[1],this.padding,this.strides[1]),r=br(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},mg=class extends h7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),l7(e,t,n,r,a,"max")}};mg.className="MaxPooling3D";re.registerClass(mg);var Ag=class extends h7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),qn(r),l7(e,t,n,r,a,"avg")}};Ag.className="AveragePooling3D";re.registerClass(Ag);var d7=class extends qe{constructor(e){super(e);this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e(s)}},bg=class extends f7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ut(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ut(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return V(()=>(e=Le(e),i7((n,r)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};bg.className="TimeDistributed";re.registerClass(bg);function ete(e){di(AJ,"BidirectionalMergeMode",e)}var tte="concat",_g=class extends f7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=wr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=wr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?tte:e.mergeMode,ete(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):_n(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=s7(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new wr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new wr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new wr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Yo))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=_r(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Yo.className="Sequential";re.registerClass(Yo);function w8(e){return new Qr(e)}function b8(e){return new Yo(e)}function _8(e,t){return t==null&&(t={}),qee(e,t)}function D0(e){return x3(e)}function v8(e,t){lr.registerCallbackConstructor(e,t)}var Dn=class extends re.Serializable{getConfig(){return{}}},H3=class extends Dn{apply(e,t=1){return CJ(e,t)}};H3.className="elu";re.registerClass(H3);var j3=class extends Dn{apply(e){return sd(e)}};j3.className="selu";re.registerClass(j3);var G3=class extends Dn{apply(e){return Rr(e)}};G3.className="relu";re.registerClass(G3);var q3=class extends Dn{apply(e){return V(()=>qo(6,Rr(e)))}};q3.className="relu6";re.registerClass(q3);var X3=class extends Dn{apply(e){return e}};X3.className="linear";re.registerClass(X3);var K3=class extends Dn{apply(e){return Qn(e)}};K3.className="sigmoid";re.registerClass(K3);var Z3=class extends Dn{apply(e){return FJ(e)}};Z3.className="hardSigmoid";re.registerClass(Z3);var Y3=class extends Dn{apply(e){return jo(e)}};Y3.className="softplus";re.registerClass(Y3);var J3=class extends Dn{apply(e){return RJ(e)}};J3.className="softsign";re.registerClass(J3);var Q3=class extends Dn{apply(e){return Bo(e)}};Q3.className="tanh";re.registerClass(Q3);var ky=class extends Dn{apply(e,t=-1){return Eu(e,t)}};ky.className="softmax";re.registerClass(ky);var e7=class extends Dn{apply(e,t=-1){return Jh(e,t)}};e7.className="logSoftmax";re.registerClass(e7);var t7=class extends Dn{apply(e,t=1){return V(()=>Qn(e.mul(t)).mul(e))}};t7.className="swish";re.registerClass(t7);function Ba(e){return e.getClassName()}function Iy(e,t={}){return uc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Va(e){if(e==null){let t={};return t.className="linear",t.config={},Iy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Iy(t)}else return e instanceof Dn?e:Iy(e)}function Ny(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var n7=class extends re.Serializable{},bc=class extends n7{constructor(e){super();Ny(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=Ct([1]);return this.hasL1&&(t=oe(t,Ee(W(this.l1,Dt(e))))),this.hasL2&&(t=oe(t,Ee(W(this.l2,fc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};bc.className="L1L2";re.registerClass(bc);function Kee(e){return Ny(e),new bc({l1:e!=null?e.l1:null,l2:0})}function Zee(e){return Ny(e),new bc({l2:e!=null?e.l2:null,l1:0})}var r7={l1l2:"L1L2"};function ct(e){return BA(e)}function a7(e,t={}){return uc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function gt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in r7?r7[e]:e,config:{}};return a7(t)}else return e instanceof n7?e:a7(e)}var Sy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Le(e);let n=Rr(e);return this.maxValue!=null&&(n=gn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="ReLU";re.registerClass(Sy);var Ty=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Le(e);return _u(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="LeakyReLU";re.registerClass(Ty);var Ey=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=yt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=gt(e.alphaRegularizer),this.alphaConstraint=Wt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ut(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(Tt(t),t==="channelsFirst"?rt(e,[0,2,3,1]):e))}function s7(e,t){return V(()=>(Tt(t),t==="channelsFirst"?rt(e,[0,2,3,4,1]):e))}function Yee(e,t,n,r=1,a="valid",s,i=1){return V(()=>{if(s==null&&(s=xr()),Tt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=rt(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=jh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Vr(o,n)),o})}function i7(e,t,n,r=[1,1],a="valid",s,i,o=null){return V(()=>{if(s==null&&(s=xr()),Tt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=My(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Sa.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=rt(l,[0,3,1,2])),l})}function Jee(e,t,n,r=[1,1,1],a="valid",s,i){return V(()=>{if(s==null&&(s=xr()),Tt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=s7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Wf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Vr(o,n)),s==="channelsFirst"&&(o=rt(o,[0,4,1,2,3])),o})}var $y=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",$y.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=El(t.kernelSize,e,"kernelSize"),this.strides=El(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=Va(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=El(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Wr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!UA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ba(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},_c=class extends $y{constructor(e,t){super(e,t);this.kernel=null,_c.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Le(e);let n,r=this.bias==null?null:this.bias.read(),a=J_(this.activation.getClassName());if(a!=null&&this.rank===2)n=i7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Yee(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=i7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Jee(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ut(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},vc=class extends _c{constructor(e){super(2,e);vc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!UA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};vc.className="Conv2D";re.registerClass(vc);var $p=class extends _c{constructor(e){super(3,e);$p.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};$p.className="Conv3D";re.registerClass($p);var Dy=class extends vc{constructor(e){super(e);if(this.inputSpec=[new Ut({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ut({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Mp(o,h,u,this.padding),f=Mp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=rt(n,[0,2,3,1]));let A=Gh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=rt(A,[0,3,1,2])),this.bias!=null&&(A=Vr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ut(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Mp(t[r],o,s,this.padding),t[a]=Mp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Dy.className="Conv2DTranspose";re.registerClass(Dy);var o7=class extends _c{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=ut(e),e.length{e=Le(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=rt(e,[0,2,3,1])),n=em(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Vr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=rt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseConstraint),e.pointwiseConstraint=Pt(this.pointwiseConstraint),e}};o7.className="SeparableConv";var Oy=class extends o7{constructor(e){super(2,e)}};Oy.className="SeparableConv2D";re.registerClass(Oy);var Dp=class extends _c{constructor(e){super(1,e);Dp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!UA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Dp.className="Conv1D";re.registerClass(Dp);var zy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=hp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=hp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Cropping2D";re.registerClass(zy);var Ly=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Le(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=rt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return rt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="UpSampling2D";re.registerClass(Ly);function Qee(e,t,n=[1,1],r="valid",a,s){return V(()=>{a==null&&(a=xr()),Tt(a);let i=My(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Vo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}var Py=class extends $y{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=ut(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Le(e);let n=Qee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Vr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=vr(t,this.kernelSize[0],this.padding,this.strides[0]),s=vr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseRegularizer),e}};Py.className="DepthwiseConv2D";re.registerClass(Py);function l7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function u7(e,t,n,r=!1,a,s,i=!1,o=!1){return V(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(br(2,l));if(t=rt(t,u),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Sn(a,-1)),a=rt(a,u)),r&&(t=Cn(t,0),a!=null&&(a=Cn(a,0)));let c=[],h,d=n,p=t.shape[0],f=nr(t),m;a!=null&&(m=nr(a));for(let y=0;ye(g,d));if(a==null)h=w[0],d=w[1];else{let b=V(()=>{let _=m[y],x=En(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,M)=>w[1][M].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Rn(c,1)),[h,A,d]})}var Mr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Op({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ut({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return br(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){uy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ut({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let r=0;rVt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=l7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ut({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Ar){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Le(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=u7((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return V(()=>{let t=Ct(e.shape);return t=Ee(t,[1,2]),t=pc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?KA(t,[1,n]):t):this.cell.stateSize>1?[KA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=_r(r,n);return new e(Object.assign(t,{cell:a}))}};Mr.className="RNN";re.registerClass(Mr);var Ac=class extends qe{},zp=class extends Ac{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Va(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Il([1,Pa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,Pa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0En(e),rate:this.dropout,training:r})),0En(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Br(W(e,s),this.kernel.read()):a=Br(e,this.kernel.read()),this.bias!=null&&(a=Vr(a,this.bias.read())),i!=null&&(n=W(n,i));let o=oe(a,Br(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ba(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};zp.className="SimpleRNNCell";re.registerClass(zp);var Wy=class extends Mr{constructor(e){e.cell=new zp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Wy.className="SimpleRNN";re.registerClass(Wy);var Lp=class extends Ac{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=Va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Il([1,Pa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,Pa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0En(e),rate:this.dropout,training:n,count:3})),0En(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};By.className="GRU";re.registerClass(By);var kc=class extends Ac{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Il([1,Pa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,Pa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ut(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),u=new pp().apply([s]),c=a.apply([s*2]);return o3(o3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0En(e),rate:this.dropout,training:n,count:4})),0En(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Vy.className="LSTM";re.registerClass(Vy);var Op=class extends Ac{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{Ai(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(_r(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return cy(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;su3(t(),n),i=()=>mc(s,t,r);return!a||a<=1?Vt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Vt(o.clone()))}var ete=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;sVt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=vr(l,r[0],a,s[0],i[0]),h=vr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};c7.className="ConvRNN2D";var Pp=class extends kc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=El(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=El(r||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=a||"valid",Xn(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=El(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=ut(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends or{apply(c,h){let d=l.apply([u]),p=Cr([u]),f=l.apply([u*2]);return YA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0En(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,ie,te)=>!ie||!ie[te]?J:W(ie[te],J),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0En(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=on(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?on(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,P,U]=on(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,D),m=this.recurrentConv(m,L),A=this.recurrentConv(A,P),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(oe(u,f)),X=this.recurrentActivation.apply(oe(c,m)),G=oe(W(X,s),W(H,this.activation.apply(oe(h,A)))),ee=W(this.recurrentActivation.apply(oe(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ete(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Kr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Vr(a,n,this.dataFormat):a}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Pp.className="ConvLSTM2DCell";re.registerClass(Pp);var Uy=class extends c7{constructor(e){let t=new Pp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Uy.className="ConvLSTM2D";re.registerClass(Uy);var Wp=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=Le(e);if(0u3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Wp.className="Dropout";re.registerClass(Wp);var Hy=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Hy.className="SpatialDropout1D";re.registerClass(Hy);var jy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Gt(this.units,"units"),this.activation=Va(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ut(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ut(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),r=J_(this.activation.getClassName()),a;return r!=null?a=Br(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Br(n,this.kernel.read()),this.bias!=null&&(a=Vr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ba(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};jy.className="Dense";re.registerClass(jy);var Gy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ut(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],La(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ba(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};qy.className="Activation";re.registerClass(qy);var Xy=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Le(e),SJ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="RepeatVector";re.registerClass(Xy);var Ky=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Le(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Reshape";re.registerClass(Ky);var Zy=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=br(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ut({ndim:this.dims.length+1})]}computeOutputShape(e){e=ut(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return rt(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Zy.className="Permute";re.registerClass(Zy);var Yy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Le(e),r=-1;return mu(Xs(n,this.maskValue),r)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),r=-1,a=!0,s=mu(Xs(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Yy.className="Masking";re.registerClass(Yy);var Jy=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(pt(e.inputLength))}this.inputDim=e.inputDim,Gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Gt(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Le(e),Xs(e,He(e))):null)}computeOutputShape(e){if(e=ut(e),this.inputLength==null)return[...e,this.outputDim];let t=pt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r{this.invokeCallHook(e,t);let n=Le(e);return n.dtype!=="int32"&&(n=dc(n,"int32")),l3(this.embeddings.read(),n.as1D()).reshape(ut(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Pt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="Embedding";re.registerClass(Jy);var bi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&za(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Pa(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=br(1,l).concat([0]);n.push(rt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=rt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(br(0,i-1));s=rt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Sn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>YA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return V(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Ic(a,e[s].shape.length)):r=[Ic(this.axes,t.shape.length),Ic(this.axes,n.shape.length)],this.normalize&&(t=Ip(t,r[0]),n=Ip(n,r[1])),tte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ic(this.axes,e.length),Ic(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};sg.className="Dot";re.registerClass(sg);var ig=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return mc(()=>dp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};ig.className="GaussianNoise";re.registerClass(ig);var og=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?mc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(dp(n.shape,1,r))},()=>n,t.training||!1):n})}};og.className="GaussianDropout";re.registerClass(og);var lg=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return mc(()=>{let r=Le(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ia(Xo(n),this.rate);o=dc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Le(e),t.training||!1)}return e})}};lg.className="AlphaDropout";re.registerClass(lg);function Nc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=X2(e,t,n,r,a,s);else if(e.rank===3)i=K2(e,t,n,r,a,s);else if(e.rank===4)i=Z2(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function nte(e,t,n,r,a=.001){return V(()=>{let s=ed(e,r),i=s.mean,o=s.variance;return[Nc(e,i,o,n,t,a),i,o]})}function rte(e,t,n,r,a=.001){return V(()=>{let s=ed(e,r),i=s.mean,o=s.variance,l=[];for(let p of br(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Nc(e,u,c,d,h,a),i,o]})}function ate(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),br(0,e.rank-1))?nte(e,t,n,r,a):rte(e,t,n,r,a)}var ug=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=ut(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ut({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,r=Le(e),a=r.shape,s=a.length,i=br(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=pi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,br(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Nc(r,A,y,g,w,this.epsilon)}else return Nc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=ate(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{V(()=>{let w=1-g,b=A.read(),_=b.sub(y).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Pt(this.betaConstraint),gammaConstraint:Pt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ug.className="BatchNormalization";re.registerClass(ug);var cg=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ut(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==za(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Le(e),r=n.shape,a=r.length;return V(()=>{let s=!0,{mean:i,variance:o}=ed(n,this.axis,s),l=pi(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=xr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Zr(e,r)})}var hg=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?xr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){e=ut(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>ste(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};hg.className="ZeroPadding2D";re.registerClass(hg);function Bp(e,t,n,r,a,s){return V(()=>{Tt(a),t3(s),Xn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=xr()),s==null&&(s="max"),e=My(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=ku(e,t,n,o):i=yu(e,t,n,o),a==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}function h7(e,t,n,r,a,s){return V(()=>{Tt(a),t3(s),Xn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=xr()),s==null&&(s="max"),e=s7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Kf(e,t,n,o):i=Lf(e,t,n,o),a==="channelsFirst"&&(i=rt(i,[0,4,1,2,3])),i})}var d7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Gt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xn(this.padding),this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){e=ut(e);let t=vr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=pc(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Na(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},dg=class extends d7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),Bp(e,t,n,r,a,"max")}};dg.className="MaxPooling1D";re.registerClass(dg);var pg=class extends d7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),Bp(e,t,n,r,a,"avg")}};pg.className="AveragePooling1D";re.registerClass(pg);var p7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Xn(this.padding),this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},fg=class extends p7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),Bp(e,t,n,r,a,"max")}};fg.className="MaxPooling2D";re.registerClass(fg);var mg=class extends p7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),Bp(e,t,n,r,a,"avg")}};mg.className="AveragePooling2D";re.registerClass(mg);var f7=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Xn(this.padding),this.inputSpec=[new Ut({ndim:5})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),r=vr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ag=class extends f7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),h7(e,t,n,r,a,"max")}};Ag.className="MaxPooling3D";re.registerClass(Ag);var yg=class extends f7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Xn(r),h7(e,t,n,r,a,"avg")}};yg.className="AveragePooling3D";re.registerClass(yg);var m7=class extends qe{constructor(e){super(e);this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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t}},qte=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[oe(k("a",e,t,n),k("b",e,t,n))];case"AddN":return[Vh(k("tensors",e,t,n))];case"FloorMod":case"Mod":return[Yf(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[W(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[ve(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[Uf(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[Bh(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[xe(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[qo(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Er(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[Yr(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[hd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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Kte=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Vt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[ot(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Us(r,ye(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[Us(s,ye(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=ka(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(ka(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:H(l,s)});return[Rn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return nr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[_a(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return on(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[p0(r,a,s)]}case"GatherNd":{let 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V(()=>ine(s,i,o));case"slice_join":return V(()=>one(s,i,o));case"spectral":return V(()=>lne(s,i,o));case"transformation":return V(()=>une(s,i,o));case"hash_table":return tne(s,i,o,r);case"custom":let l=y7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Wte(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function j7(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((U7(d)||cne(d)||hne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function dne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var pne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],fne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],mne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function U7(e){return pne.indexOf(e.op)>=0}function cne(e){return fne.indexOf(e.op)>=0}function hne(e){return mne.indexOf(e.op)>=0}var zg=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zg(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=j7(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return dne(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return V(()=>{let c=new V7(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=On(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;fkn(f,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=bte(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new V7(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>kn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=j7(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[w,b]=On(g),_=[];_[b]=e[g],p[w]=_});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!U7(g)&&!kn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=ia(c.node.name,n)),r[c.node.name]==null){let d=B7(c.node,r,n,this._resourceManager);h||([h]=ia(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ia(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ane=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},yne="?tfjs-format=file",gne="model.json",L0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ane}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=yn.browserHTTPRequest(e,this.loadOptions);else{let t=yn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(yn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=yn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new zg(O7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=O7.Instance.transformGraph(e.modelInitializer);this.initializer=new zg(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=yn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ye)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Yn(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Bne=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Une=class extends qt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return 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qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},tv=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=hr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=hr.getTensorsInContainer(n);for(let a of t)hr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Wg=class extends qt{constructor(){super();this.outputQueue=new Lg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new sv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),dr(n,t)}},iv=class extends qt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=pr([s,a,o,i],[1,4])}else this.cropBox=pr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new iv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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qp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Xp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function dv(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return St.cropAndResize(t,s,[0],n)}function Vg(e,t=1.6){let n=Xp(e),r=qp(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function Ug(e){let t=Xp(e),n=qp(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,landmarks:e.landmarks}}var jg=[[1,0,0],[0,1,0],[0,0,1]];function cre(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function hre(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return cre(n)}function pv(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function xi(e,t){let n=0;for(let 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s=0;s{s.box.startPoint.dispose(),s.box.endPoint.dispose(),s.landmarks.dispose()});let a=V(()=>this.storedBoxes.map((s,i)=>{let o,l=0,u;if(t.face.detector.rotation){let[w,b]=s.landmarks.length>=gre?wre:vre;l=hre(s.landmarks[w],s.landmarks[b]);let _=Xp({startPoint:s.startPoint,endPoint:s.endPoint}),x=[_[0]/e.shape[2],_[1]/e.shape[1]],N=St.rotateWithOffset(e,l,0,x);u=mv(-l,_),o=dv({startPoint:s.startPoint,endPoint:s.endPoint},N,[this.meshHeight,this.meshWidth]).div(255)}else{u=jg;let w=e.clone();o=dv({startPoint:s.startPoint,endPoint:s.endPoint},w,[this.meshHeight,this.meshWidth]).div(255)}if(!t.face.mesh.enabled)return{coords:null,box:s,faceConfidence:null,confidence:s.confidence,image:o};let[,c,h]=this.meshDetector.predict(o),d=c.dataSync()[0];if(ds!==null),this.detectedFaces=a.length,a}calculateLandmarksBoundingBox(e){let 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n=[],r=St.resizeBilinear(e,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),a=r.toInt(),s,i;if(t.profile){let o=await Lo(()=>this.model.executeAsync(a));s=o.result[0].dataSync(),i=o.result[1].squeeze().arraySync(),o.result.forEach(l=>l.dispose()),Sc("faceboxes",o)}else{let[o,l,u]=await this.model.executeAsync(a);s=o.dataSync();let c=l.squeeze();i=c.arraySync(),o.dispose(),l.dispose(),c.dispose(),u.dispose()}a.dispose(),r.dispose();for(let o in i)if(s[o]&&s[o]>this.config.face.detector.minConfidence){let l=[i[o][0]/this.enlarge,i[o][1]/this.enlarge,i[o][2]*this.enlarge,i[o][3]*this.enlarge],u=[l[1],l[0],l[3]-l[1],l[2]-l[0]],c=[parseInt((u[0]*e.shape[2]).toString()),parseInt((u[1]*e.shape[1]).toString()),parseInt((u[2]*e.shape[2]).toString()),parseInt((u[3]*e.shape[1]).toString())],h=St.cropAndResize(e,[l],[0],[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),d=h.div([255]);h.dispose(),n.push({confidence:s[o],box:c,boxRaw:this.config.face.mesh.returnRawData?u:null,image:d})}return n}};async function Sre(e){let t=await Yn(e.face.detector.modelPath);e.debug&&Ve(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`);let n=new xv(t,e);return e.face.mesh.enabled&&e.debug&&Ve(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ve(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),n}var bv={};kr(bv,{load:()=>Yg,predict:()=>Jg});var Cl,Zp={age:0},Yp=Number.MAX_SAFE_INTEGER;async function Yg(e){return 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$re=class{constructor(e){this.model=e}predict(e,t){return V(()=>{let n=(t.body.modelType==="ResNet"?e.toFloat().add(Cre):e.toFloat().div(127.5).sub(1)).expandDims(0),r=this.model.predict(n).map(s=>s.squeeze([0])),a=t.body.modelType==="ResNet"?Fre(r):Rre(r);return{heatmapScores:a.heatmap.sigmoid(),offsets:a.offsets,displacementFwd:a.displacementFwd,displacementBwd:a.displacementBwd}})}dispose(){this.model.dispose()}};function u2(e){return Math.floor(e/2)}var Mre=class{constructor(e,t){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=t}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return 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r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),n.dispose(),a}dispose(){this.baseModel.dispose()}};async function l2(e){let t=await Yn(e.body.modelPath),n=new $re(t);return e.debug&&Ve(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new Tv(n)}var Lv={};kr(Lv,{HandPose:()=>Pv,load:()=>p2});function f2(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function e1(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function nae(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return St.cropAndResize(t,s,[0],n)}function rae(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function m2(e,t=1.5){let n=e1(e),r=f2(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function A2(e){let t=e1(e),n=f2(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,palmLandmarks:e.palmLandmarks}}var aae=class{constructor(e,t,n){this.model=e,this.anchors=n.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=pr(this.anchors),this.inputSizeTensor=tn([t,t]),this.doubleInputSizeTensor=tn([t*2,t*2])}normalizeBoxes(e){return V(()=>{let t=Fe(e,[0,0],[-1,2]),n=Fe(e,[0,2],[-1,2]),r=oe(ve(t,this.inputSizeTensor),this.anchorsTensor),a=ve(n,this.doubleInputSizeTensor),s=W(xe(r,a),this.inputSizeTensor),i=W(oe(r,a),this.inputSizeTensor);return Uh([s,i],1)})}normalizeLandmarks(e,t){return V(()=>{let n=oe(ve(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[t]);return W(n,this.inputSizeTensor)})}async getBoxes(e,t){let n=this.model.predict(e),r=n.squeeze();n.dispose();let a=V(()=>Qn(Fe(r,[0,0],[-1,1])).squeeze()),s=a.dataSync(),i=Fe(r,[0,1],[-1,4]),o=this.normalizeBoxes(i);i.dispose();let l=await St.nonMaxSuppressionAsync(o,s,t.hand.maxHands,t.hand.iouThreshold,t.hand.scoreThreshold),u=l.arraySync();a.dispose(),l.dispose();let c=[];for(let h of u)if(s[h]>=t.hand.minConfidence){let d=Fe(o,[h,0],[1,-1]),p=Fe(r,[h,5],[1,14]),f=V(()=>this.normalizeLandmarks(p,h).reshape([-1,2]));p.dispose(),c.push({box:d,palmLandmarks:f,confidence:s[h]})}return r.dispose(),o.dispose(),c}async estimateHandBounds(e,t){let n=e.shape[1],r=e.shape[2],a=V(()=>e.resizeBilinear([t.hand.inputSize,t.hand.inputSize]).div(127.5).sub(1)),s=await this.getBoxes(a,t);a.dispose();let i=[];if(!s||s.length===0)return i;for(let o of s){let l=o.box.dataSync(),u=l.slice(0,2),c=l.slice(2,4),h=o.palmLandmarks.arraySync();o.box.dispose(),o.palmLandmarks.dispose(),i.push(rae({startPoint:u,endPoint:c,palmLandmarks:h,confidence:o.confidence},[r/t.hand.inputSize,n/t.hand.inputSize]))}return i}};function sae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function iae(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return sae(n)}var Wv=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function bi(e,t){let n=0;for(let r=0;rUv([...a,1],t)),r=this.calculateLandmarksBoundingBox(n);return m2(A2(r),uae)}getBoxForHandLandmarks(e){let t=this.calculateLandmarksBoundingBox(e),n=m2(A2(t),jv);n.palmLandmarks=[];for(let r=0;r[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),o=Vv(n,[0,0]),l=i.map(d=>[...Uv(d,o),d[2]]),u=lae(r),c=[...e1(t),1],h=[bi(c,u[0]),bi(c,u[1])];return l.map(d=>[d[0]+h[0],d[1]+h[1],d[2]])}async estimateHands(e,t){let n=!1,r;(this.skipped===0||this.skipped>t.hand.skipFrames||!t.hand.landmarks||!t.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,t),this.skipped=0),t.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==t.hand.maxHands||!t.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];t.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let s=0;s=t.hand.minConfidence){let g=H(A,[-1,3]),w=g.arraySync();A.dispose(),g.dispose();let b=this.transformRawCoords(w,d,o,h),_=this.getBoxForHandLandmarks(b);this.storedBoxes[s]=_;let x={landmarks:b,confidence:y,box:{topLeft:_.startPoint,bottomRight:_.endPoint}};a.push(x)}else this.storedBoxes[s]=null;A.dispose()}else{let o=m2(A2(i),jv),l={confidence:i.confidence,box:{topLeft:o.startPoint,bottomRight:o.endPoint}};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(s=>s!==null),this.detectedHands=a.length,a}calculateLandmarksBoundingBox(e){let 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${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var fae=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},mae=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let s=e[n].mesh[152][2];Math.abs(s)>10&&t.push({face:n,gesture:`head ${s<0?"up":"down"}`})}return t},Aae=e=>{if(!e)return[];let t=[];for(let n=0;n{if(!e)return[];let t=[];for(let n=0;n0){let a=r.reduce((i,o)=>i.position[2]i.position[1](u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in 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r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},lne=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Nt.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Nt.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=k("image",e,t,n),a=k("boxes",e,t,n),s=k("boxInd",e,t,n),i=k("cropSize",e,t,n),o=k("method",e,t,n),l=k("extrapolationValue",e,t,n);return[Nt.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},une=(e,t,n)=>{switch(e.op){case"Equal":return[va(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[Xs(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[er(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[Ia(k("a",e,t,n),k("b",e,t,n))];case"Less":return[Zh(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[qs(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[tr(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[vu(k("a",e,t,n))];case"LogicalOr":return[Qh(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[xn(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},cne=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ge(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[rt(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=k("args",e,t,n);return[Sa.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not 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r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[Iu(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[gu(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[Bf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[xu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function H7(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return V(()=>qte(s,i,o));case"basic_math":return V(()=>Xte(s,i,o));case"control":return ene(s,i,o);case"convolution":return V(()=>tne(s,i,o));case"creation":return V(()=>nne(s,i,o));case"dynamic":return rne(s,i,o);case"evaluation":return V(()=>ane(s,i,o));case"image":return V(()=>lne(s,i,o));case"graph":return V(()=>sne(s,i,o));case"logical":return V(()=>une(s,i,o));case"matrices":return V(()=>cne(s,i,o));case"normalization":return V(()=>hne(s,i,o));case"reduction":return V(()=>dne(s,i,o));case"slice_join":return V(()=>pne(s,i,o));case"spectral":return V(()=>fne(s,i,o));case"transformation":return V(()=>mne(s,i,o));case"hash_table":return one(s,i,o,r);case"custom":let l=w7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Gte(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function q7(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((G7(d)||Ane(d)||yne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function gne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var xne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],wne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],bne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function G7(e){return xne.indexOf(e.op)>=0}function Ane(e){return wne.indexOf(e.op)>=0}function yne(e){return bne.indexOf(e.op)>=0}var Lg=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Lg(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=q7(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return gne(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return V(()=>{let c=new j7(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=On(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;fkn(f,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Ste(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new j7(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>kn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=q7(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[w,b]=On(g),_=[];_[b]=e[g],p[w]=_});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!G7(g)&&!kn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=ia(c.node.name,n)),r[c.node.name]==null){let d=H7(c.node,r,n,this._resourceManager);h||([h]=ia(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ia(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},_ne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},vne="?tfjs-format=file",kne="model.json",B0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new _ne}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=yn.browserHTTPRequest(e,this.loadOptions);else{let t=yn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(yn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=yn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Lg(P7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=P7.Instance.transformGraph(e.modelInitializer);this.initializer=new Lg(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=yn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ye)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Vn(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${kne}${vne}`);let n=new B0(e,t);return await n.load(),n}var S8="3.2.0",V0={};ze(V0,{CSVDataset:()=>K7,Dataset:()=>Cl,FileDataSource:()=>Z7,TextLineDataset:()=>X7,URLDataSource:()=>Y7,array:()=>Ine,csv:()=>Sne,func:()=>Tne,generator:()=>Ene,microphone:()=>Rne,version_data:()=>Fne,webcam:()=>Cne,zip:()=>Nne});var Mne=Jo(U0()),$ne=Jo(U0());function Dne(e,t){return jp(e,t)}function jp(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Rl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=jp(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function One(e,t=Q7){return J7(e,t)}function J7(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Rl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(u=>u[i]),l=J7(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function Q7(e){return e===null?null:Rl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function ev(e,t){let n=new Map;jp(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return jp(e,t,n)}function Rl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ye))}function Lne(e){return e==null||zne(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ye||v.isTypedArray(e)}function zne(e){return e===null||typeof e!="object"&&typeof e!="function"}function Wne(e){return Dne(e,Pne)}function Pne(e){return e instanceof Ye?{value:e.clone(),recurse:!1}:Rl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var tv=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},Pg=class extends tv{constructor(){super(Pg.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new Kne(this,e,t)}columnMajorBatch(e,t=!0,n=Q7){return this.rowMajorBatch(e,t).map(r=>One(r,n))}concatenate(e,t){return new rv(nv([this,e]),t)}take(e){return e<0||e==null?this:new Xne(this,e)}skip(e){return e<0||e==null?this:new qne(this,e)}prefetch(e){return new sv(this,e)}shuffle(e,t){return new ere(this,e,t)}serial(){return new Gne(this)}},Bne=class extends qt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Wne(e),done:!1}}},Vne=class extends qt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},Gne=class extends qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},qne=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Kne=class extends qt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Zne=class extends qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},Yne=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),r=pr.getTensorsInContainer(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Jne=class extends qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},av=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=pr.getTensorsInContainer(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Bg=class extends qt{constructor(){super();this.outputQueue=new Pg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Qne=class extends Bg{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),r=pr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return!0}},rv=class extends qt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Ha;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Ha||(Ha={}));var Hne=class extends qt{constructor(e,t=Ha.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await ev(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ha.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ha.SHORTEST:return{value:null,done:!0};case Ha.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},sv=class extends qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new tv(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},ere=class extends sv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=$ne.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Cl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,tre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(r=>V(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let r=Wg(async()=>({value:await t.iterator(),done:!1}));return Une(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Mne.alea(t||v.now().toString());return zn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,zn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Cl.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends Cl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Ine(e){return zn(async()=>nv(e),e.length)}function Nne(e){if(!Rl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await ev(e,r=>{if(r instanceof Cl)return{value:r.iterator(),recurse:!1};if(Rl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return jne(n,Ha.SHORTEST)},t)}function tre(e){if(e===null)return null;let t=e[0];return Lne(t)?{value:nre(e),recurse:!1}:{value:null,recurse:!0}}function nre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ye?Rn(e):fr(e)}var X7=class extends Cl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Gp='"',Ec=Symbol("out"),iv=Symbol("field"),qp=Symbol("quote"),Vg=Symbol("quoteafterquote"),ov=Symbol("quoteinquote"),K7=class extends Cl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new X7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new lv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),fr(n,t)}},uv=class extends qt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=mr([s,a,o,i],[1,4])}else this.cropBox=mr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new uv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pu.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=Sn(ye(e,"float32"),0),n;n=Nt.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},cv=class{},hv=class extends qt{split(e){return new rre(this,e)}},rre=class extends hv{constructor(e,t){super();this.upstream=e,this.impl=new are(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},are=class extends Bg{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},ire=class extends qt{decodeUTF8(){return new sre(this)}},sre=class extends hv{constructor(e){super();this.upstream=e,this.impl=new ore(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ore=class extends Bg{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ck();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},dv=class extends ire{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function ure(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=lre(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new dv(s,t)}else throw new Error(a.statusText)}var lre=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function pv(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Z7=class extends cv{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(pv(this.input)&&Y().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new dv(this.input,this.options)}},Y7=class extends cv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return pv(this.url)?new Z7(this.url,this.fileOptions).iterator():ure(this.url,this.fileOptions)}};function Sne(e,t={}){return new K7(new Y7(e),t)}function Tne(e){let t=Wg(e);return zn(async()=>t)}function Ene(e){return zn(async()=>{let t=await e();return Wg(()=>t.next())})}async function Cne(e,t){return uv.create(e,t)}async function Rne(e){return lv.create(e)}var Fne="3.2.0",T8={tfjs:hk,"tfjs-core":dk,"tfjs-data":pk,"tfjs-layers":fk,"tfjs-converter":mk,"tfjs-backend-cpu":b0,"tfjs-backend-webgl":k0,"tfjs-backend-wasm":S0},Ln={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function cre(){if(!G2(Ln.name)){Ve("backend registration:",Ln.name);try{Ln.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ln.width,Ln.height):document.createElement("canvas")}catch(e){Ve("error: cannot create canvas:",e);return}try{Ln.gl=Ln.canvas.getContext("webgl2",Ln.webGLattr)}catch(e){Ve("error: cannot get WebGL2 context:",e);return}try{hm(2,Ln.gl)}catch(e){Ve("error: cannot set WebGL2 context:",e);return}try{let e=new dm(Ln.gl);fu(Ln.name,()=>new Mu(e),Ln.priority)}catch(e){Ve("error: cannot register WebGL backend:",e);return}try{hu("webgl").forEach(e=>{let t={...e,backendName:Ln.name};Po(t)})}catch(e){Ve("error: cannot update WebGL backend registration:",e);return}try{ql.set("WEBGL_VERSION",2)}catch(e){Ve("error: cannot set WebGL backend flags:",e);return}Ve("backend registered:",Ln.name)}}var fv=6;function hre(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r({startEndTensor:e,startPoint:Fe(e,[0,0],[-1,2]),endPoint:Fe(e,[0,2],[-1,2])});function pre(e,t,n){let r=Fe(e,[0,1],[-1,2]),a=oe(r,t),s=Fe(e,[0,3],[-1,2]),i=ve(s,n),o=ve(a,n),l=ve(i,2),u=xe(o,l),c=oe(o,l),h=W(u,n),d=W(c,n);return Hh([h,d],1)}var fre=class{constructor(e,t){this.blazeFaceModel=e,this.width=t.face.detector.inputSize,this.height=t.face.detector.inputSize,this.anchorsData=hre(t.face.detector.inputSize),this.anchors=mr(this.anchorsData),this.inputSize=tn([this.width,this.height]),this.config=t,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[t,n,r]=V(()=>{let u=e.resizeBilinear([this.width,this.height]),c=xe(u.div(127.5),1),h=this.blazeFaceModel.predict(c),d;if(Array.isArray(h)){let A=h.sort((w,b)=>w.size-b.size),y=ot([A[0],A[2]],2),g=ot([A[1],A[3]],2);d=ot([g,y],1).squeeze(0)}else d=h.squeeze();let p=pre(d,this.anchors,this.inputSize),f=Fe(d,[0,0],[-1,1]),m=Qn(f).squeeze();return[d,p,m]}),a=await Nt.nonMaxSuppressionAsync(n,r,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),s=a.arraySync();a.dispose();let i=s.map(u=>Fe(n,[u,0],[1,-1])).map(u=>{let c=u.arraySync();return u.dispose(),c}),o=r.dataSync(),l=[];for(let u=0;uthis.config.face.detector.minConfidence){let d=dre(i[u]),p=this.anchorsData[c],f=V(()=>Fe(t,[c,fv-1],[1,-1]).squeeze().reshape([fv,-1]));l.push({box:d,landmarks:f,anchor:p,confidence:h})}}return t.dispose(),n.dispose(),r.dispose(),t.dispose(),{boxes:l,scaleFactor:[e.shape[2]/this.width,e.shape[1]/this.height]}}};async function v4(e){let t=await Vn(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new fre(t,e);return e.debug&&Ve(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function mre(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function Xp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Kp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function mv(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Nt.cropAndResize(t,s,[0],n)}function Ug(e,t=1.6){let n=Kp(e),r=Xp(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function Hg(e){let t=Kp(e),n=Xp(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,landmarks:e.landmarks}}var jg=[[1,0,0],[0,1,0],[0,0,1]];function Are(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function yre(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Are(n)}function Av(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function _i(e,t){let n=0;for(let r=0;rGg[e]),Ece=_re.map(e=>Gg[e]),Cce=vre.map(e=>Gg[e]),kre=468,Ire=13,Nre=[Ire,ya.midwayBetweenEyes[0]],Sre=3,Tre=2,Ere=[Sre,Tre],qg=ya.leftEyeLower0,Xg=[qg[0],qg[qg.length-1]],Kg=ya.rightEyeLower0,Zg=[Kg[0],Kg[Kg.length-1]],Cre=3,Rre=4,Fre=71,Yg=76;function Zp(e,t,n,r=null){for(let a=0;a[s[0]*(h[0]-this.meshWidth/2),s[1]*(h[1]-this.meshHeight/2),h[2]]),o=n!==0?gv(n,[0,0]):jg,l=n!==0?i.map(h=>[...wre(h,o),h[2]]):i,u=n!==0?xre(r):jg,c=[...Kp({startPoint:t.startPoint,endPoint:t.endPoint}),1];return l.map(h=>[h[0]+_i(c,u[0]),h[1]+_i(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(e){let t=e[Xg[0]][2],n=e[Zg[0]][2];return t-n}getEyeBox(e,t,n,r,a=!1){let s=Hg(Ug(this.calculateLandmarksBoundingBox([e[n],e[r]]),this.irisEnlarge)),i=Xp(s),o=Nt.cropAndResize(t,[[s.startPoint[1]/this.meshHeight,s.startPoint[0]/this.meshWidth,s.endPoint[1]/this.meshHeight,s.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return a&&(o=Nt.flipLeftRight(o)),{box:s,boxSize:i,crop:o}}getEyeCoords(e,t,n,r=!1){let a=[];for(let s=0;s{let l=s;return o===2?l=r:o===4&&(l=a),[i[0],i[1],l]})}async predict(e,t){let n=!1,r;if((this.skipped===0||this.skipped>t.face.detector.skipFrames||!t.face.mesh.enabled||!t.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(e),this.skipped=0),t.videoOptimized&&this.skipped++,r&&r.boxes&&(!t.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==t.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let s of r.boxes)this.storedBoxes.push({startPoint:s.box.startPoint.dataSync(),endPoint:s.box.endPoint.dataSync(),landmarks:s.landmarks,confidence:s.confidence});this.storedBoxes.length>0&&(n=!0)}if(t.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),n){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let s=0;s{s.box.startPoint.dispose(),s.box.endPoint.dispose(),s.landmarks.dispose()});let a=V(()=>this.storedBoxes.map((s,i)=>{let o,l=0,u;if(t.face.detector.rotation){let[w,b]=s.landmarks.length>=kre?Nre:Ere;l=yre(s.landmarks[w],s.landmarks[b]);let _=Kp({startPoint:s.startPoint,endPoint:s.endPoint}),x=[_[0]/e.shape[2],_[1]/e.shape[1]],N=Nt.rotateWithOffset(e,l,0,x);u=gv(-l,_),o=mv({startPoint:s.startPoint,endPoint:s.endPoint},N,[this.meshHeight,this.meshWidth]).div(255)}else{u=jg;let w=e.clone();o=mv({startPoint:s.startPoint,endPoint:s.endPoint},w,[this.meshHeight,this.meshWidth]).div(255)}if(!t.face.mesh.enabled)return{coords:null,box:s,faceConfidence:null,confidence:s.confidence,image:o};let[,c,h]=this.meshDetector.predict(o),d=c.dataSync()[0];if(ds!==null),this.detectedFaces=a.length,a}calculateLandmarksBoundingBox(e){let t=e.map(s=>s[0]),n=e.map(s=>s[1]),r=[Math.min(...t),Math.min(...n)],a=[Math.max(...t),Math.max(...n)];return{startPoint:r,endPoint:a,landmarks:e}}},wv=ah(N4()),bv={};dr(bv,{FaceBoxes:()=>_v,load:()=>Mre});var vv={};function Fl(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};vv[e]=i,Ve("Human profiler",e,i)}var _v=class{constructor(e,t){this.enlarge=1.1,this.model=e,this.config=t}async estimateFaces(e,t){t&&(this.config=t);let n=[],r=Nt.resizeBilinear(e,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),a=r.toInt(),s,i;if(t.profile){let o=await Hs(()=>this.model.executeAsync(a));s=o.result[0].dataSync(),i=o.result[1].squeeze().arraySync(),o.result.forEach(l=>l.dispose()),Fl("faceboxes",o)}else{let[o,l,u]=await this.model.executeAsync(a);s=o.dataSync();let c=l.squeeze();i=c.arraySync(),o.dispose(),l.dispose(),c.dispose(),u.dispose()}a.dispose(),r.dispose();for(let o in i)if(s[o]&&s[o]>this.config.face.detector.minConfidence){let l=[i[o][0]/this.enlarge,i[o][1]/this.enlarge,i[o][2]*this.enlarge,i[o][3]*this.enlarge],u=[l[1],l[0],l[3]-l[1],l[2]-l[0]],c=[parseInt((u[0]*e.shape[2]).toString()),parseInt((u[1]*e.shape[1]).toString()),parseInt((u[2]*e.shape[2]).toString()),parseInt((u[3]*e.shape[1]).toString())],h=Nt.cropAndResize(e,[l],[0],[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),d=h.div([255]);h.dispose(),n.push({confidence:s[o],box:c,boxRaw:this.config.face.mesh.returnRawData?u:null,image:d})}return n}};async function Mre(e){let t=await Vn(e.face.detector.modelPath);e.debug&&Ve(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`);let n=new _v(t,e);return e.face.mesh.enabled&&e.debug&&Ve(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ve(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),n}var kv={};dr(kv,{load:()=>Jg,predict:()=>Qg});var Ml,Yp={age:0},Jp=Number.MAX_SAFE_INTEGER;async function Jg(e){return Ml||(Ml=await Vn(e.face.age.modelPath),e.debug&&Ve(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),Ml}async function Qg(e,t){return Ml?Jp0?(Jp++,Yp):(t.videoOptimized?Jp=0:Jp=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Nt.resizeBilinear(e,[t.face.age.inputSize,t.face.age.inputSize],!1),a=W(r,[255]);Re(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await Ml.predict(a));else{let o=t.face.age.enabled?await Hs(()=>Ml.predict(a)):{};s=o.result.clone(),o.result.dispose(),Fl("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),Yp=i,n(i)})):null}var Iv={};dr(Iv,{load:()=>e2,predict:()=>t2});var vi,n2={gender:""},Qp=Number.MAX_SAFE_INTEGER,r2=!1,a2=[.2989,.587,.114];async function e2(e){return vi||(vi=await Vn(e.face.gender.modelPath),r2=vi.inputs[0].shape[3]===1,e.debug&&Ve(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),vi}async function t2(e,t){return vi?Qp{let r=Nt.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;r2?a=V(()=>{let[o,l,u]=on(r,3,3),c=W(o,a2[0]),h=W(l,a2[1]),d=W(u,a2[2]);return Vh([c,h,d]).sub(.5).mul(2)}):a=W(r,[255]),Re(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await vi.predict(a));else{let o=t.face.gender.enabled?await Hs(()=>vi.predict(a)):{};s=o.result.clone(),o.result.dispose(),Fl("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(r2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l))}}s.dispose(),n2=i,n(i)})):null}var Nv={};dr(Nv,{load:()=>s2,predict:()=>i2});var $re=["angry","disgust","fear","happy","sad","surprise","neutral"],$l,o2=[],e1=Number.MAX_SAFE_INTEGER,l2=[.2989,.587,.114],Sv=1;async function s2(e){return $l||($l=await Vn(e.face.emotion.modelPath),e.debug&&Ve(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),$l}async function i2(e,t){return $l?e10?(e1++,o2):(t.videoOptimized?e1=0:e1=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Nt.resizeBilinear(e,[t.face.emotion.inputSize,t.face.emotion.inputSize],!1),[a,s,i]=on(r,3,3);r.dispose();let o=W(a,l2[0]),l=W(s,l2[1]),u=W(i,l2[2]);a.dispose(),s.dispose(),i.dispose();let c=Vh([o,l,u]);o.dispose(),l.dispose(),u.dispose();let h=V(()=>c.sub(.5).mul(2));c.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let f=await Hs(()=>$l.predict(h));p=f.result.dataSync(),f.result.dispose(),Fl("emotion",f)}else{let f=await $l.predict(h);p=f.dataSync(),Re(f)}for(let f=0;ft.face.emotion.minConfidence&&d.push({score:Math.min(.99,Math.trunc(100*Sv*p[f])/100),emotion:$re[f]});d.sort((f,m)=>m.score-f.score)}h.dispose(),o2=d,n(d)})):null}var Dl;async function Tv(e){return Dl||(Dl=await Vn(e.face.embedding.modelPath),e.debug&&Ve(`load model: 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Pre=class{constructor(e){this.model=e}predict(e,t){return V(()=>{let n=(t.body.modelType==="posenet-resnet"?e.toFloat().add(Ore):e.toFloat().div(127.5).sub(1)).expandDims(0),r=this.model.predict(n).map(s=>s.squeeze([0])),a=t.body.modelType==="posenet-resnet"?Lre(r):zre(r);return{heatmapScores:a.heatmap.sigmoid(),offsets:a.offsets,displacementFwd:a.displacementFwd,displacementBwd:a.displacementBwd}})}dispose(){this.model.dispose()}};function c2(e){return Math.floor(e/2)}var Wre=class{constructor(e,t){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=t}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(c2(e),e);)this.exchange(e,c2(e)),e=c2(e)}sink(e){for(;2*e<=this.numberOfElements;){let t=2*e;if(tt){l=!1;break}if(!l)break}return l}function Vre(e,t,n){let[r,a,s]=n.shape,i=new Wre(r*a*s,({score:o})=>o);for(let o=0;on?n:e}function Hre(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function Dv(e,t){return{x:e.x+t.x,y:e.y+t.y}}var h2=ah(vf());function jre(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;ae.toTensor().mul(ke(t,"int32")).toFloat().add(qre(e,n)))}function Kre(e,t){return V(()=>{let n=e.div(ke(t,"int32"));return e.sub(n.mul(ke(t,"int32")))})}function Zre(e){let[t,n,r]=e.shape;return V(()=>{let a=e.reshape([t*n,r]).argMax(0),s=a.div(ke(n,"int32")).expandDims(1),i=Kre(a,n).expandDims(1);return ot([s,i],1)})}var Ov=Ol.poseChain.map(([e,t])=>[Ol.partIds[e],Ol.partIds[t]]),d2=Ov.map(([,e])=>e),zv=Ov.map(([e])=>e),Yre=16;function Jre(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function p2(e,t,n,r){return{y:$v(Math.round(e.y/t),0,n-1),x:$v(Math.round(e.x/t),0,r-1)}}function Lv(e,t,n,r,a,s,i,o=2){let[l,u]=r.shape,c=p2(t.position,s,l,u),h=Jre(e,c,i),d=Dv(t.position,h);for(let m=0;m=0;--d){let p=d2[d],f=zv[d];l[p]&&!l[f]&&(l[f]=Lv(d,l[p],f,t,n,r,s))}for(let d=0;d(r+=p,{position:{y:c.get(f,0),x:c.get(f,1)},part:Ol.partNames[f],score:p})),d=h.filter(p=>p.score>n.body.scoreThreshold);return a.dispose(),u.dispose(),{keypoints:d,score:r/h.length}}var tae=1,Pv=16;function Wv(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return Hre(r,n,i.y,i.x)<=t})}function nae(e,t,n){return n.reduce((r,{position:a,score:s},i)=>(Wv(e,t,a,i)||(r+=s),r),0)/n.length}function rae(e,t,n,r,a){let s=[],i=Vre(a.body.scoreThreshold,tae,e),o=a.body.nmsRadius^2;for(;s.lengtha.body.scoreThreshold&&s.push({keypoints:c,score:h})}return s}async function aae(e){return Promise.all(e.map(t=>t.buffer()))}function sae(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:s.x*n,y:s.y*t}}))}}function iae(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function Bv(e,[t,n],[r,a]){return e.map(s=>sae(s,t/r,n/a))}async function oae(e,t,n){return new Promise(async r=>{let a=e.shape[1],s=e.shape[2],i=await aae([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),o=i[0],l=i[1],u=i[2],c=i[3],h=await rae(o,l,u,c,n),d=Bv(h,[a,s],[n.body.inputSize,n.body.inputSize]);r(d)})}async function lae(e,t,n){return new Promise(async r=>{let a=e.shape[1],s=e.shape[2],i=[await eae(t.heatmapScores,t.offsets,n)],o=Bv(i,[a,s],[n.body.inputSize,n.body.inputSize]);r(o)})}var Rv=class{constructor(e){this.baseModel=e}async estimatePoses(e,t){let n=iae(e,[t.body.inputSize,t.body.inputSize]),r=this.baseModel.predict(n,t),a=t.body.maxDetections<2?await lae(e,r,t):await oae(e,r,t);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),n.dispose(),a}dispose(){this.baseModel.dispose()}};async function u2(e){let t=await Vn(e.body.modelPath),n=new Pre(t);return e.debug&&Ve(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new Rv(n)}var Vv={};dr(Vv,{HandPose:()=>Uv,load:()=>f2});function m2(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function t1(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function uae(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Nt.cropAndResize(t,s,[0],n)}function cae(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function A2(e,t=1.5){let n=t1(e),r=m2(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function y2(e){let t=t1(e),n=m2(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,palmLandmarks:e.palmLandmarks}}var hae=class{constructor(e,t,n){this.model=e,this.anchors=n.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=mr(this.anchors),this.inputSizeTensor=tn([t,t]),this.doubleInputSizeTensor=tn([t*2,t*2])}normalizeBoxes(e){return V(()=>{let t=Fe(e,[0,0],[-1,2]),n=Fe(e,[0,2],[-1,2]),r=oe(ve(t,this.inputSizeTensor),this.anchorsTensor),a=ve(n,this.doubleInputSizeTensor),s=W(xe(r,a),this.inputSizeTensor),i=W(oe(r,a),this.inputSizeTensor);return Hh([s,i],1)})}normalizeLandmarks(e,t){return V(()=>{let n=oe(ve(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[t]);return W(n,this.inputSizeTensor)})}async getBoxes(e,t){let n=this.model.predict(e),r=n.squeeze();n.dispose();let a=V(()=>Qn(Fe(r,[0,0],[-1,1])).squeeze()),s=a.dataSync(),i=Fe(r,[0,1],[-1,4]),o=this.normalizeBoxes(i);i.dispose();let l=await Nt.nonMaxSuppressionAsync(o,s,t.hand.maxHands,t.hand.iouThreshold,t.hand.scoreThreshold),u=l.arraySync();a.dispose(),l.dispose();let c=[];for(let h of u)if(s[h]>=t.hand.minConfidence){let d=Fe(o,[h,0],[1,-1]),p=Fe(r,[h,5],[1,14]),f=V(()=>this.normalizeLandmarks(p,h).reshape([-1,2]));p.dispose(),c.push({box:d,palmLandmarks:f,confidence:s[h]})}return r.dispose(),o.dispose(),c}async estimateHandBounds(e,t){let n=e.shape[1],r=e.shape[2],a=V(()=>e.resizeBilinear([t.hand.inputSize,t.hand.inputSize]).div(127.5).sub(1)),s=await this.getBoxes(a,t);a.dispose();let i=[];if(!s||s.length===0)return i;for(let o of s){let l=o.box.dataSync(),u=l.slice(0,2),c=l.slice(2,4),h=o.palmLandmarks.arraySync();o.box.dispose(),o.palmLandmarks.dispose(),i.push(cae({startPoint:u,endPoint:c,palmLandmarks:h,confidence:o.confidence},[r/t.hand.inputSize,n/t.hand.inputSize]))}return i}};function dae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function pae(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return dae(n)}var Hv=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ki(e,t){let n=0;for(let r=0;rqv([...a,1],t)),r=this.calculateLandmarksBoundingBox(n);return A2(y2(r),Aae)}getBoxForHandLandmarks(e){let t=this.calculateLandmarksBoundingBox(e),n=A2(y2(t),Xv);n.palmLandmarks=[];for(let r=0;r[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),o=Gv(n,[0,0]),l=i.map(d=>[...qv(d,o),d[2]]),u=mae(r),c=[...t1(t),1],h=[ki(c,u[0]),ki(c,u[1])];return l.map(d=>[d[0]+h[0],d[1]+h[1],d[2]])}async estimateHands(e,t){let n=!1,r;(this.skipped===0||this.skipped>t.hand.skipFrames||!t.hand.landmarks||!t.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,t),this.skipped=0),t.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==t.hand.maxHands||!t.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];t.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let s=0;s=t.hand.minConfidence){let g=j(A,[-1,3]),w=g.arraySync();A.dispose(),g.dispose();let b=this.transformRawCoords(w,d,o,h),_=this.getBoxForHandLandmarks(b);this.storedBoxes[s]=_;let x={landmarks:b,confidence:y,box:{topLeft:_.startPoint,bottomRight:_.endPoint}};a.push(x)}else this.storedBoxes[s]=null;A.dispose()}else{let o=A2(y2(i),Xv),l={confidence:i.confidence,box:{topLeft:o.startPoint,bottomRight:o.endPoint}};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(s=>s!==null),this.detectedHands=a.length,a}calculateLandmarksBoundingBox(e){let 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${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var Zv={};dr(Zv,{load:()=>x2,predict:()=>w2});var cr,bae=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"];async function x2(e){return cr||(cr=await Vn(e.body.modelPath),cr.width=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[2].size),cr.height=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ve(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),cr}async function w2(e,t){if(!cr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Nt.resizeBilinear(e,[cr.width||t.body.inputSize,cr.height||t.body.inputSize],!1),a=ve(r,[255]);r.dispose();let s;if(t.profile){let o=await Hs(()=>cr.predict(a));s=o.result.find(l=>l.size===195).dataSync(),o.result.forEach(l=>l.dispose()),Fl("blazepose",o)}else{let o=await cr.predict(a);s=o.find(l=>l.size===195||l.size===155).dataSync(),o.forEach(l=>l.dispose())}a.dispose();let i=[];for(let o=0;o{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},vae=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing 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loaded"}return null}simmilarity(e,t){return this.config.face.embedding.enabled?Ere(e,t):0}async load(e=null){this.state="load";let t=ht();e&&(this.config=Tc(this.config,e)),this.firstRun&&(this.config.debug&&Ve(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ve("configuration:",this.config),this.config.debug&&Ve("tf flags:",this.tf.ENV.flags)));let n=this.config.face.detector.modelPath.includes("faceboxes")?gv:yv;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?n.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Yg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Qg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?a2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Iv(this.config):null),this.models.posenet||(this.config.body.enabled?l2(this.config):null),this.models.handpose||(this.config.hand.enabled?p2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await n.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Yg(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Qg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await a2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Iv(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await l2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await p2(this.config))),this.firstRun&&(this.config.debug&&Ve("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(ht()-t);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let t=ht();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Ve("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Ve("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Ve("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&are();try{await this.tf.setBackend(this.config.backend)}catch(n){Ve("error: cannot set backend:",this.config.backend,n)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ve("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let n=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ve(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ht()-t)}}async detectFace(e){var t,n,r,a,s,i;let o,l,u,c,h,d=[];this.state="run:face",o=ht();let p=await((t=this.models.face)==null?void 0:t.estimateFaces(e,this.config));this.perf.face=Math.trunc(ht()-o);for(let f of p){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Ve("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?l=this.config.face.age.enabled?Jg(f.image,this.config):{}:(this.state="run:age",o=ht(),l=this.config.face.age.enabled?await Jg(f.image,this.config):{},this.perf.age=Math.trunc(ht()-o)),this.analyze("Start Gender:"),this.config.async?u=this.config.face.gender.enabled?e2(f.image,this.config):{}:(this.state="run:gender",o=ht(),u=this.config.face.gender.enabled?await e2(f.image,this.config):{},this.perf.gender=Math.trunc(ht()-o)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?s2(f.image,this.config):{}:(this.state="run:emotion",o=ht(),c=this.config.face.emotion.enabled?await s2(f.image,this.config):{},this.perf.emotion=Math.trunc(ht()-o)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?h=this.config.face.embedding.enabled?Nv(f.image,this.config):[]:(this.state="run:embedding",o=ht(),h=this.config.face.embedding.enabled?await Nv(f.image,this.config):[],this.perf.embedding=Math.trunc(ht()-o)),this.analyze("End Emotion:"),this.config.async&&([l,u,c,h]=await Promise.all([l,u,c,h])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((n=f==null?void 0:f.annotations)==null?void 0:n.leftEyeIris)&&((r=f==null?void 0:f.annotations)==null?void 0:r.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let m=((a=f.annotations)==null?void 0:a.leftEyeIris)&&((s=f.annotations)==null?void 0:s.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;d.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:l.age,gender:u.gender,genderConfidence:u.confidence,emotion:c,embedding:h,iris:m!==0?Math.trunc(m)/100:0}),(i=f.image)==null||i.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),d}async detect(e,t={}){return new Promise(async n=>{var r,a,s,i;this.state="config";let o;this.config=Tc(this.config,t),this.state="check";let l=this.sanity(e);l&&(Ve(l,e),n({error:l}));let u,c,h,d=ht();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),o=ht();let p=wae(e,this.config);if(!p||!p.tensor){Ve("could not convert input to tensor"),n({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ht()-o),this.analyze("Get Image:"),this.config.async?(h=this.config.face.enabled?this.detectFace(p.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",o=ht(),h=this.config.face.enabled?await this.detectFace(p.tensor):[],this.perf.face=Math.trunc(ht()-o)),this.analyze("Start Body:"),this.config.async?(u=this.config.body.enabled?(r=this.models.posenet)==null?void 0:r.estimatePoses(p.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",o=ht(),u=this.config.body.enabled?await((a=this.models.posenet)==null?void 0:a.estimatePoses(p.tensor,this.config)):[],this.perf.body=Math.trunc(ht()-o)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(s=this.models.handpose)==null?void 0:s.estimateHands(p.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",o=ht(),c=this.config.hand.enabled?await((i=this.models.handpose)==null?void 0:i.estimateHands(p.tensor,this.config)):[],this.perf.hand=Math.trunc(ht()-o)),this.analyze("End Hand:"),this.config.async&&([h,u,c]=await Promise.all([h,u,c])),p.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(o=ht(),f=[...mae(h),...fae(u),...yae(c),...Aae(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ht()-o)),this.perf.total=Math.trunc(ht()-d),this.state="idle",n({face:h,body:u,hand:c,gesture:f,performance:this.perf,canvas:p.canvas})})}async warmupBitmap(){let e=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(s=>s.blob()),t,n;switch(this.config.warmup){case"face":t=await e(g2);break;case"full":t=await e(x2);break;default:t=null}if(t){let r=await createImageBitmap(t);n=await this.detect(r,this.config),r.close()}return n}async warmupCanvas(){return new Promise(e=>{let t,n=0;switch(this.config.warmup){case"face":n=256,t="data:image/jpeg;base64,"+g2;break;case"full":n=1200,t="data:image/jpeg;base64,"+x2;break;default:t=null}let r=new Image(n,n);r.onload=()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(n,n):document.createElement("canvas");a.width=n,a.height=n;let s=a.getContext("2d");s==null||s.drawImage(r,0,0);let i=s==null?void 0:s.getImageData(0,0,n,n);this.detect(i,this.config).then(o=>e(o))},t?r.src=t:e(null)})}async warmupNode(){let e=s=>Buffer.from(s,"base64"),t=this.config.warmup==="face"?e(g2):e(x2),n=(void 0).decodeJpeg(t),r=n.expandDims(0);this.tf.dispose(n);let a=await this.detect(r,this.config);return this.tf.dispose(r),a}async warmup(e){let t=ht();e&&(this.config=Tc(this.config,e));let n=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=n;let a=ht();return this.config.debug&&Ve("Warmup",this.config.warmup,Math.round(a-t),"ms",r),r}};async function vae(e,t,n){if(!e)return;let r=t.getContext("2d");r.font=n.baseFont,r.fillStyle=n.baseLabel;let a=1;for(let s=0;s1&&o[1].length>0){let l=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${l}: ${o[1]}`;r.fillStyle="black",r.fillText(u,8,2+a*n.baseLineHeight),r.fillStyle=n.baseLabel,r.fillText(u,6,0+a*n.baseLineHeight),a+=1}}}async function kae(e,t,n,r){if(!e)return;let a=t.getContext("2d");for(let s of e){a.font=n.baseFont,a.strokeStyle=n.baseColor,a.fillStyle=n.baseColor,a.lineWidth=n.baseLineWidth,a.beginPath(),n.drawBoxes&&a.rect(s.box[0],s.box[1],s.box[2],s.box[3]);let i=[];if(i.push(`detect confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}i.length===0&&i.push("face"),a.fillStyle=n.baseLabel;for(let o=i.length-1;o>=0;o--){a.fillStyle="black";let l=Math.max(s.box[0],0),u=o*n.baseLineHeight+s.box[1];a.fillText(i[o],l+5,u+16),a.fillStyle=n.baseLabel,a.fillText(i[o],l+4,u+15)}if(a.fillStyle=n.baseColor,a.stroke(),a.lineWidth=1,s.mesh){if(n.drawPoints)for(let o of s.mesh)a.fillStyle=n.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:n.baseColor,a.beginPath(),a.arc(o[0],o[1],2,0,2*Math.PI),a.fill();if(n.drawPolygons){for(let o=0;os.mesh[c]),u=new Path2D;u.moveTo(l[0][0],l[0][1]);for(let c of l)u.lineTo(c[0],c[1]);u.closePath(),a.strokeStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.stroke(u),n.fillPolygons&&(a.fillStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.fill(u))}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}}}}}var Ua=[];async function Iae(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a=0;al.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightShoulder"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftShoulder"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftWrist"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightWrist"),o&&s.lineTo(o.position.x,o.position.y)),r.stroke(s)}}}async function Nae(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a of e){if(r.font=n.baseFont,r.lineWidth=n.baseLineWidth,n.drawBoxes&&(r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.baseColor,r.fillStyle=n.baseColor,r.rect(a.box[0],a.box[1],a.box[2],a.box[3]),r.fillStyle="black",r.fillText("hand",a.box[0]+3,1+a.box[1]+n.baseLineHeight,a.box[2]),r.fillStyle=n.baseLabel,r.fillText("hand",a.box[0]+2,0+a.box[1]+n.baseLineHeight,a.box[2]),r.stroke()),n.drawPoints&&a.landmarks&&a.landmarks.length>0)for(let s of a.landmarks)r.fillStyle=n.useDepth?`rgba(${127.5+2*s[2]}, ${127.5-2*s[2]}, 255, 0.5)`:n.baseColor,r.beginPath(),r.arc(s[0],s[1],2,0,2*Math.PI),r.fill();if(n.drawPolygons){let s=i=>{if(!!i)for(let o=0;o0?o-1:0][0],i[o>0?o-1:0][1]),r.lineTo(i[o][0],i[o][1]),r.stroke()};s(a.annotations.indexFinger),s(a.annotations.middleFinger),s(a.annotations.ringFinger),s(a.annotations.pinky),s(a.annotations.thumb)}}}var Ec={face:kae,body:Iae,hand:Nae,gesture:vae};var 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",o6={url:"https://github.com/vladmandic/human/issues"},l6="https://github.com/vladmandic/human#readme",u6="MIT",c6={node:">=12.0.0"},h6={type:"git",url:"git+https://github.com/vladmandic/human.git"},d6={},p6={},f6={"@tensorflow/tfjs":"^3.2.0","@tensorflow/tfjs-backend-cpu":"^3.2.0","@tensorflow/tfjs-backend-wasm":"^3.2.0","@tensorflow/tfjs-backend-webgl":"^3.2.0","@tensorflow/tfjs-converter":"^3.2.0","@tensorflow/tfjs-core":"^3.2.0","@tensorflow/tfjs-data":"^3.2.0","@tensorflow/tfjs-layers":"^3.2.0","@tensorflow/tfjs-node":"^3.2.0","@tensorflow/tfjs-node-gpu":"^3.2.0","@types/node":"^14.14.31","@typescript-eslint/eslint-plugin":"^4.16.1","@typescript-eslint/parser":"^4.16.1","@vladmandic/pilogger":"^0.2.14",chokidar:"^3.5.1",dayjs:"^1.10.4",esbuild:"^0.8.54",eslint:"^7.21.0","eslint-config-airbnb-base":"^14.2.1","eslint-plugin-import":"^2.22.1","eslint-plugin-json":"^2.1.2","eslint-plugin-node":"^11.1.0","eslint-plugin-promise":"^4.3.1",rimraf:"^3.0.2",seedrandom:"^3.0.5","simple-git":"^2.35.2",tslib:"^2.1.0",typescript:"^4.3.0-dev.20210302"},m6={start:"node 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e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Cc(s,i):n[a]=i}),n),{})}var y6=class{constructor(e={}){this.tf=z2,this.package=Jv,this.version=v2,this.config=Cc(Tae,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=t=>Yv(t,this.config),this.facemesh=wv,this.age=kv,this.gender=Iv,this.emotion=Nv,this.body=this.config.body.modelType.startsWith("posenet")?Cv:Zv,this.hand=Vv}profile(){return this.config.profile?vv:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let t=this.tf.engine().state.numTensors,n=this.numTensors;this.numTensors=t;let r=t-n;r!==0&&Ve(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(t){return"backend not loaded"}return null}simmilarity(e,t){return this.config.face.embedding.enabled?Dre(e,t):0}async load(e=null){this.state="load";let t=ht();e&&(this.config=Cc(this.config,e)),this.firstRun&&(this.config.debug&&Ve(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ve("configuration:",this.config),this.config.debug&&Ve("tf flags:",this.tf.ENV.flags)));let n=this.config.face.detector.modelPath.includes("faceboxes")?bv:wv;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?n.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Jg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?e2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?s2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Tv(this.config):null),this.models.handpose||(this.config.hand.enabled?f2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?u2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?x2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await n.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Jg(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await e2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await s2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Tv(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await f2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await u2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await x2(this.config))),this.firstRun&&(this.config.debug&&Ve("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(ht()-t);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let t=ht();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Ve("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Ve("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Ve("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&cre();try{await this.tf.setBackend(this.config.backend)}catch(n){Ve("error: cannot set backend:",this.config.backend,n)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ve("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let n=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ve(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ht()-t)}}async detectFace(e){var t,n,r,a,s,i;let o,l,u,c,h,d=[];this.state="run:face",o=ht();let p=await((t=this.models.face)==null?void 0:t.estimateFaces(e,this.config));this.perf.face=Math.trunc(ht()-o);for(let f of p){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Ve("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?l=this.config.face.age.enabled?Qg(f.image,this.config):{}:(this.state="run:age",o=ht(),l=this.config.face.age.enabled?await Qg(f.image,this.config):{},this.perf.age=Math.trunc(ht()-o)),this.analyze("Start Gender:"),this.config.async?u=this.config.face.gender.enabled?t2(f.image,this.config):{}:(this.state="run:gender",o=ht(),u=this.config.face.gender.enabled?await t2(f.image,this.config):{},this.perf.gender=Math.trunc(ht()-o)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?i2(f.image,this.config):{}:(this.state="run:emotion",o=ht(),c=this.config.face.emotion.enabled?await i2(f.image,this.config):{},this.perf.emotion=Math.trunc(ht()-o)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?h=this.config.face.embedding.enabled?Ev(f.image,this.config):[]:(this.state="run:embedding",o=ht(),h=this.config.face.embedding.enabled?await Ev(f.image,this.config):[],this.perf.embedding=Math.trunc(ht()-o)),this.analyze("End Emotion:"),this.config.async&&([l,u,c,h]=await Promise.all([l,u,c,h])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((n=f==null?void 0:f.annotations)==null?void 0:n.leftEyeIris)&&((r=f==null?void 0:f.annotations)==null?void 0:r.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let m=((a=f.annotations)==null?void 0:a.leftEyeIris)&&((s=f.annotations)==null?void 0:s.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;d.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:l.age,gender:u.gender,genderConfidence:u.confidence,emotion:c,embedding:h,iris:m!==0?Math.trunc(m)/100:0}),(i=f.image)==null||i.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),d}async detect(e,t={}){return new Promise(async n=>{var r,a,s,i;this.state="config";let o;this.config=Cc(this.config,t),this.state="check";let l=this.sanity(e);l&&(Ve(l,e),n({error:l}));let u,c,h,d=ht();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),o=ht();let p=Yv(e,this.config);if(!p||!p.tensor){Ve("could not convert input to tensor"),n({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ht()-o),this.analyze("Get Image:"),this.config.async?(h=this.config.face.enabled?this.detectFace(p.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",o=ht(),h=this.config.face.enabled?await this.detectFace(p.tensor):[],this.perf.face=Math.trunc(ht()-o)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?u=this.config.body.enabled?(r=this.models.posenet)==null?void 0:r.estimatePoses(p.tensor,this.config):[]:u=this.config.body.enabled?w2(p.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",o=ht(),this.config.body.modelType.startsWith("posenet")?u=this.config.body.enabled?await((a=this.models.posenet)==null?void 0:a.estimatePoses(p.tensor,this.config)):[]:u=this.config.body.enabled?await w2(p.tensor,this.config):[],this.perf.body=Math.trunc(ht()-o)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(s=this.models.handpose)==null?void 0:s.estimateHands(p.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",o=ht(),c=this.config.hand.enabled?await((i=this.models.handpose)==null?void 0:i.estimateHands(p.tensor,this.config)):[],this.perf.hand=Math.trunc(ht()-o)),this.analyze("End Hand:"),this.config.async&&([h,u,c]=await Promise.all([h,u,c])),p.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(o=ht(),f=[...vae(h),..._ae(u),...Iae(c),...kae(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ht()-o)),this.perf.total=Math.trunc(ht()-d),this.state="idle",n({face:h,body:u,hand:c,gesture:f,performance:this.perf,canvas:p.canvas})})}async warmupBitmap(){let e=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(s=>s.blob()),t,n;switch(this.config.warmup){case"face":t=await e(b2);break;case"full":t=await e(_2);break;default:t=null}if(t){let r=await createImageBitmap(t);n=await this.detect(r,this.config),r.close()}return n}async warmupCanvas(){return new Promise(e=>{let t,n=0;switch(this.config.warmup){case"face":n=256,t="data:image/jpeg;base64,"+b2;break;case"full":case"body":n=1200,t="data:image/jpeg;base64,"+_2;break;default:t=null}let r=new Image;r.onload=async()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(n,n):document.createElement("canvas");a.width=r.naturalWidth,a.height=r.naturalHeight;let s=a.getContext("2d");s==null||s.drawImage(r,0,0);let i=await this.detect(a,this.config);e(i)},t?r.src=t:e(null)})}async warmupNode(){let e=s=>Buffer.from(s,"base64"),t=this.config.warmup==="face"?e(b2):e(_2),n=(void 0).decodeJpeg(t),r=n.expandDims(0);this.tf.dispose(n);let a=await this.detect(r,this.config);return this.tf.dispose(r),a}async warmup(e){let t=ht();e&&(this.config=Cc(this.config,e));let n=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=n;let a=ht();return this.config.debug&&Ve("Warmup",this.config.warmup,Math.round(a-t),"ms",r),r}};async function Cae(e,t=0,n=0,r=0,a="black",s){let i=e.getContext("2d");i.fillStyle=a,i.beginPath(),i.arc(t,n,r,0,2*Math.PI),i.fill(),s&&i.fillText(s,t+4,n+4)}async function Rae(e,t,n){if(!e)return;let r=t.getContext("2d");r.font=n.baseFont,r.fillStyle=n.baseLabel;let a=1;for(let s=0;s1&&o[1].length>0){let l=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${l}: ${o[1]}`;r.fillStyle="black",r.fillText(u,8,2+a*n.baseLineHeight),r.fillStyle=n.baseLabel,r.fillText(u,6,0+a*n.baseLineHeight),a+=1}}}async function Fae(e,t,n,r){if(!e)return;let a=t.getContext("2d");for(let s of e){a.font=n.baseFont,a.strokeStyle=n.baseColor,a.fillStyle=n.baseColor,a.lineWidth=n.baseLineWidth,a.beginPath(),n.drawBoxes&&a.rect(s.box[0],s.box[1],s.box[2],s.box[3]);let i=[];if(i.push(`detect confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}i.length===0&&i.push("face"),a.fillStyle=n.baseLabel;for(let o=i.length-1;o>=0;o--){a.fillStyle="black";let l=Math.max(s.box[0],0),u=o*n.baseLineHeight+s.box[1];a.fillText(i[o],l+5,u+16),a.fillStyle=n.baseLabel,a.fillText(i[o],l+4,u+15)}if(a.fillStyle=n.baseColor,a.stroke(),a.lineWidth=1,s.mesh){if(n.drawPoints)for(let o of s.mesh)a.fillStyle=n.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:n.baseColor,a.beginPath(),a.arc(o[0],o[1],2,0,2*Math.PI),a.fill();if(n.drawPolygons){for(let o=0;os.mesh[c]),u=new Path2D;u.moveTo(l[0][0],l[0][1]);for(let c of l)u.lineTo(c[0],c[1]);u.closePath(),a.strokeStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.stroke(u),n.fillPolygons&&(a.fillStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.fill(u))}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}}}}}var ja=[];async function Mae(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a=0;al.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightShoulder"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftShoulder"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftAnkle"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftHeel"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftFoot"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightAnkle"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightHeel"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightFoot"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftWrist"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftPalm"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightWrist"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightPalm"),o&&s.lineTo(o.position.x,o.position.y)),r.stroke(s)}}}async function $ae(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a of e){if(r.font=n.baseFont,r.lineWidth=n.baseLineWidth,n.drawBoxes&&(r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.baseColor,r.fillStyle=n.baseColor,r.rect(a.box[0],a.box[1],a.box[2],a.box[3]),r.fillStyle="black",r.fillText("hand",a.box[0]+3,1+a.box[1]+n.baseLineHeight,a.box[2]),r.fillStyle=n.baseLabel,r.fillText("hand",a.box[0]+2,0+a.box[1]+n.baseLineHeight,a.box[2]),r.stroke()),n.drawPoints&&a.landmarks&&a.landmarks.length>0)for(let s of a.landmarks)r.fillStyle=n.useDepth?`rgba(${127.5+2*s[2]}, ${127.5-2*s[2]}, 255, 0.5)`:n.baseColor,r.beginPath(),r.arc(s[0],s[1],2,0,2*Math.PI),r.fill();if(n.drawPolygons){let s=i=>{if(!!i)for(let o=0;o0?o-1:0][0],i[o>0?o-1:0][1]),r.lineTo(i[o][0],i[o][1]),r.stroke()};s(a.annotations.indexFinger),s(a.annotations.middleFinger),s(a.annotations.ringFinger),s(a.annotations.pinky),s(a.annotations.thumb)}}}var la={face:Fae,body:Mae,hand:$ae,gesture:Rae,point:Cae};var Rc=0,g6=!1,xt={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function Dae(){if(g6)return;let e=` :root { --rounded: 0.2rem; } .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10; box-shadow: 0 0 8px dimgrey; background: ${xt.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; } @@ -4818,10 +4818,10 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; } .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; } - `,t=document.createElement("style");t.innerHTML=e,document.getElementsByTagName("head")[0].appendChild(t),d6=!0}var p6=class{constructor(t,n,r,a){a&&(xt={...xt,...a}),Sae(),this.createMenu(t,n,r),this.id=0,this.instance=Cc,Cc++,this._maxFPS=0,this.hidden=0}createMenu(t,n="",r={top:null,left:null,bottom:null,right:null}){this.menu=document.createElement("div"),this.menu.id=`menu-${Cc}`,this.menu.className="menu",r&&(r.top&&(this.menu.style.top=r.top),r.bottom&&(this.menu.style.bottom=r.bottom),r.left&&(this.menu.style.left=r.left),r.right&&(this.menu.style.right=r.right)),this.container=document.createElement("div"),this.container.id=`menu-container-${Cc}`,this.container.className="menu-container menu-container-fadein";let a=document.createElement("div");a.className="menu-title",a.id=`menu-title-${Cc}`;let s=` + `,t=document.createElement("style");t.innerHTML=e,document.getElementsByTagName("head")[0].appendChild(t),g6=!0}var x6=class{constructor(t,n,r,a){a&&(xt={...xt,...a}),Dae(),this.createMenu(t,n,r),this.id=0,this.instance=Rc,Rc++,this._maxFPS=0,this.hidden=0}createMenu(t,n="",r={top:null,left:null,bottom:null,right:null}){this.menu=document.createElement("div"),this.menu.id=`menu-${Rc}`,this.menu.className="menu",r&&(r.top&&(this.menu.style.top=r.top),r.bottom&&(this.menu.style.bottom=r.bottom),r.left&&(this.menu.style.left=r.left),r.right&&(this.menu.style.right=r.right)),this.container=document.createElement("div"),this.container.id=`menu-container-${Rc}`,this.container.className="menu-container menu-container-fadein";let a=document.createElement("div");a.className="menu-title",a.id=`menu-title-${Rc}`;let s=` - `;n&&(a.innerHTML=`${n}${s}`),this.menu.appendChild(a),a.addEventListener("click",()=>{this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.menu.style.borderStyle=this.container.classList.contains("menu-container-fadeout")?"none":"solid"}),this.menu.appendChild(this.container),typeof t=="object"?t.appendChild(this.menu):document.getElementById(t).appendChild(this.menu)}get newID(){return this.id++,`menu-${this.instance}-${this.id}`}get ID(){return`menu-${this.instance}-${this.id}`}get width(){return this.menu.offsetWidth}get height(){return this.menu.offsetHeight}hide(){this.container.classList.contains("menu-container-fadein")&&(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"))}visible(){return this.container.classList.contains("menu-container-fadein")}toggle(t){if(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.container.classList.contains("menu-container-fadein")&&t){let n=t.x||(t.touches&&t.touches[0]?t.touches[0].pageX:null);n&&(this.menu.style.left=`${n-this.menu.offsetWidth/2}px`),this.menu.offsetLeft<0&&(this.menu.style.left=0),this.menu.offsetLeft+this.menu.offsetWidth>window.innerWidth&&(this.menu.style.left=null,this.menu.style.right=0),this.menu.style.borderStyle="solid"}else this.menu.style.borderStyle="none"}addTitle(t){let n=document.createElement("div");return n.className="menu-title",n.id=this.newID,n.innerHTML=t,this.menu.appendChild(n),n.addEventListener("click",()=>{this.hidden=!this.hidden;let r=document.getElementsByClassName("menu");for(let a of r)a.style.display=this.hidden?"none":"block"}),n}addLabel(t){let n=document.createElement("div");return n.className="menu-item menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`
${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=``;return s.innerHTML=`
${t}`,s.style.fontFamily=document.body.style.fontFamily,s.style.fontSize=document.body.style.fontSize,s.style.fontVariant=document.body.style.fontVariant,this.container.appendChild(s),s.addEventListener("change",o=>{a&&a(n[o.target.selectedIndex])}),s}addRange(t,n,r,a,s,i,o){let l=document.createElement("div");return l.className="menu-item",l.innerHTML=`${t}`,this.container.appendChild(l),l.addEventListener("change",u=>{n[r]=parseInt(u.target.value)===parseFloat(u.target.value)?parseInt(u.target.value):parseFloat(u.target.value),u.target.setAttribute("value",u.target.value),o&&o(u.target.value)}),l.input=l.children[0],l}addHTML(t){let n=document.createElement("div");return n.className="menu-item",n.id=this.newID,t&&(n.innerHTML=t),this.container.appendChild(n),n}addButton(t,n,r){let a=document.createElement("button");return a.className="menu-item menu-button",a.style.fontFamily=document.body.style.fontFamily,a.style.fontSize=document.body.style.fontSize,a.style.fontVariant=document.body.style.fontVariant,a.type="button",a.id=this.newID,a.innerText=t,this.container.appendChild(a),a.addEventListener("click",()=>{a.innerText===t?a.innerText=n:a.innerText=t,r&&r(a.innerText!==t)}),a}addValue(t,n,r=""){let a=document.createElement("div");return a.className="menu-item",a.id=`menu-val-${t}`,a.innerText=`${t}: ${n}${r}`,this.container.appendChild(a),a}updateValue(t,n,r=""){let a=document.getElementById(`menu-val-${t}`);a?a.innerText=`${t}: ${n}${r}`:this.addValue(t,n)}addChart(t,n,r=150,a=40,s){s&&(xt.chartColor=s);let i=document.createElement("div");return i.className="menu-item menu-chart-title",i.id=this.newID,i.innerHTML=`${t}`,this.container.appendChild(i),i}async updateChart(t,n){if(!n||n.length===0)return;let r=document.getElementById(`menu-canvas-${t}`);if(!r)return;let a=r.getContext("2d");a.fillStyle=xt.background,a.fillRect(0,0,r.width,r.height);let s=r.width/n.length,i=1+Math.max(...n),o=r.height/i;for(let l=0;l`;n&&(a.innerHTML=`${n}${s}`),this.menu.appendChild(a),a.addEventListener("click",()=>{this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.menu.style.borderStyle=this.container.classList.contains("menu-container-fadeout")?"none":"solid"}),this.menu.appendChild(this.container),typeof t=="object"?t.appendChild(this.menu):document.getElementById(t).appendChild(this.menu)}get newID(){return this.id++,`menu-${this.instance}-${this.id}`}get ID(){return`menu-${this.instance}-${this.id}`}get width(){return this.menu.offsetWidth}get height(){return this.menu.offsetHeight}hide(){this.container.classList.contains("menu-container-fadein")&&(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"))}visible(){return this.container.classList.contains("menu-container-fadein")}toggle(t){if(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.container.classList.contains("menu-container-fadein")&&t){let n=t.x||(t.touches&&t.touches[0]?t.touches[0].pageX:null);n&&(this.menu.style.left=`${n-this.menu.offsetWidth/2}px`),this.menu.offsetLeft<0&&(this.menu.style.left=0),this.menu.offsetLeft+this.menu.offsetWidth>window.innerWidth&&(this.menu.style.left=null,this.menu.style.right=0),this.menu.style.borderStyle="solid"}else this.menu.style.borderStyle="none"}addTitle(t){let n=document.createElement("div");return n.className="menu-title",n.id=this.newID,n.innerHTML=t,this.menu.appendChild(n),n.addEventListener("click",()=>{this.hidden=!this.hidden;let r=document.getElementsByClassName("menu");for(let a of r)a.style.display=this.hidden?"none":"block"}),n}addLabel(t){let n=document.createElement("div");return n.className="menu-item menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`
${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=``;return s.innerHTML=`
${t}`,s.style.fontFamily=document.body.style.fontFamily,s.style.fontSize=document.body.style.fontSize,s.style.fontVariant=document.body.style.fontVariant,this.container.appendChild(s),s.addEventListener("change",o=>{a&&a(n[o.target.selectedIndex])}),s}addRange(t,n,r,a,s,i,o){let l=document.createElement("div");return l.className="menu-item",l.innerHTML=`${t}`,this.container.appendChild(l),l.addEventListener("change",u=>{n[r]=parseInt(u.target.value)===parseFloat(u.target.value)?parseInt(u.target.value):parseFloat(u.target.value),u.target.setAttribute("value",u.target.value),o&&o(u.target.value)}),l.input=l.children[0],l}addHTML(t){let n=document.createElement("div");return n.className="menu-item",n.id=this.newID,t&&(n.innerHTML=t),this.container.appendChild(n),n}addButton(t,n,r){let a=document.createElement("button");return a.className="menu-item menu-button",a.style.fontFamily=document.body.style.fontFamily,a.style.fontSize=document.body.style.fontSize,a.style.fontVariant=document.body.style.fontVariant,a.type="button",a.id=this.newID,a.innerText=t,this.container.appendChild(a),a.addEventListener("click",()=>{a.innerText===t?a.innerText=n:a.innerText=t,r&&r(a.innerText!==t)}),a}addValue(t,n,r=""){let a=document.createElement("div");return a.className="menu-item",a.id=`menu-val-${t}`,a.innerText=`${t}: ${n}${r}`,this.container.appendChild(a),a}updateValue(t,n,r=""){let a=document.getElementById(`menu-val-${t}`);a?a.innerText=`${t}: ${n}${r}`:this.addValue(t,n)}addChart(t,n,r=150,a=40,s){s&&(xt.chartColor=s);let i=document.createElement("div");return i.className="menu-item menu-chart-title",i.id=this.newID,i.innerHTML=`${t}`,this.container.appendChild(i),i}async updateChart(t,n){if(!n||n.length===0)return;let r=document.getElementById(`menu-canvas-${t}`);if(!r)return;let a=r.getContext("2d");a.fillStyle=xt.background,a.fillRect(0,0,r.width,r.height);let s=r.width/n.length,i=1+Math.max(...n),o=r.height/i;for(let l=0;l 00 FPS @@ -4851,15 +4851,15 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ - `,f6=class{constructor(t,n={}){this.css=Tae,this.svg=Eae,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(c,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-c;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(c,h,d)=>{let p=h.now();c.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},u="drawElements";t[u]?t[u]=l(t[u],this,t):console.log("bench: cannot attach to webgl function")}if(!this.withoutUI){this.dom||(this.dom=document.body);let o=document.createElement("div");o.id="gl-bench",this.dom.appendChild(o),this.dom.insertAdjacentHTML("afterbegin",'"),this.dom=o,this.dom.addEventListener("click",()=>{this.trackGPU=!this.trackGPU,this.updateUI()}),this.paramLogger=((l,u,c)=>{let h=["gl-cpu","gl-gpu","gl-mem","gl-fps","gl-gpu-svg","gl-chart"],d={...h};return h.forEach(p=>d[p]=u.getElementsByClassName(p)),this.nodes=d,(p,f,m,A,y,g,w)=>{d["gl-cpu"][p].style.strokeDasharray=(f*.27).toFixed(0)+" 100",d["gl-gpu"][p].style.strokeDasharray=(m*.27).toFixed(0)+" 100",d["gl-mem"][p].innerHTML=c[p]?c[p]:A?"mem: "+A.toFixed(0)+"mb":"",d["gl-fps"][p].innerHTML="FPS: "+y.toFixed(1),l(c[p],f,m,A,y,g,w)}})(this.paramLogger,this.dom,this.names),this.chartLogger=((l,u)=>{let c={"gl-chart":u.getElementsByClassName("gl-chart")};return(h,d,p)=>{let f="",m=d.length;for(let A=0;A=1e3){let a=this.frameId-this.paramFrame,s=a/r*1e3;for(let i=0;i{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o0&&((r=e==null?void 0:e.face[0].embedding)==null?void 0:r.length)!==192)return;vi||(vi=e,document.getElementById("compare-canvas").getContext("2d").drawImage(vi.canvas,0,0,200,200));let t=ae.simmilarity((a=vi==null?void 0:vi.face[0])==null?void 0:a.embedding,(s=e==null?void 0:e.face[0])==null?void 0:s.embedding);document.getElementById("simmilarity").innerText=`simmilarity: ${Math.trunc(1e3*t)/10}%`}var A6=performance.now();async function r1(e){let t=n1,n=document.getElementById("canvas");ie.drawFPS.push(1e3/(performance.now()-A6)),ie.drawFPS.length>ie.maxFPSframes&&ie.drawFPS.shift(),A6=performance.now(),await Ae.process.updateChart("FPS",ie.detectFPS),(ie.buffered||!t.canvas)&&(t.canvas=await ae.image(e,Ur));let r=n.getContext("2d");r.fillStyle=ie.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),await Ec.face(t.face,n,ie,ae.facemesh.triangulation),await Ec.body(t.body,n,ie),await Ec.hand(t.hand,n,ie),await Ec.gesture(t.gesture,n,ie),await Rae(t);let a=ae.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*ie.detectFPS.reduce((h,d)=>h+d,0)/ie.detectFPS.length)/10,u=Math.trunc(10*ie.drawFPS.reduce((h,d)=>h+d,0)/ie.drawFPS.length)/10,c=ie.detectFPS.length>5&&l<5?'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models':"";document.getElementById("log").innerHTML=` - video: ${ie.camera.name} | facing: ${ie.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ie.camera.width} x ${ie.camera.height} ${o}
- backend: ${ae.tf.getBackend()} | ${i}
- performance: ${Cae(t.performance)}ms FPS process:${l} refresh:${u}
+ `,w6=class{constructor(t,n={}){this.css=Oae,this.svg=zae,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(c,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-c;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(c,h,d)=>{let p=h.now();c.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},u="drawElements";t[u]?t[u]=l(t[u],this,t):console.log("bench: cannot attach to webgl function")}if(!this.withoutUI){this.dom||(this.dom=document.body);let o=document.createElement("div");o.id="gl-bench",this.dom.appendChild(o),this.dom.insertAdjacentHTML("afterbegin",'"),this.dom=o,this.dom.addEventListener("click",()=>{this.trackGPU=!this.trackGPU,this.updateUI()}),this.paramLogger=((l,u,c)=>{let h=["gl-cpu","gl-gpu","gl-mem","gl-fps","gl-gpu-svg","gl-chart"],d={...h};return h.forEach(p=>d[p]=u.getElementsByClassName(p)),this.nodes=d,(p,f,m,A,y,g,w)=>{d["gl-cpu"][p].style.strokeDasharray=(f*.27).toFixed(0)+" 100",d["gl-gpu"][p].style.strokeDasharray=(m*.27).toFixed(0)+" 100",d["gl-mem"][p].innerHTML=c[p]?c[p]:A?"mem: "+A.toFixed(0)+"mb":"",d["gl-fps"][p].innerHTML="FPS: "+y.toFixed(1),l(c[p],f,m,A,y,g,w)}})(this.paramLogger,this.dom,this.names),this.chartLogger=((l,u)=>{let c={"gl-chart":u.getElementsByClassName("gl-chart")};return(h,d,p)=>{let f="",m=d.length;for(let A=0;A=1e3){let a=this.frameId-this.paramFrame,s=a/r*1e3;for(let i=0;i{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o0&&((r=e==null?void 0:e.face[0].embedding)==null?void 0:r.length)!==192)return;Ni||(Ni=e,document.getElementById("compare-canvas").getContext("2d").drawImage(Ni.canvas,0,0,200,200));let t=se.simmilarity((a=Ni==null?void 0:Ni.face[0])==null?void 0:a.embedding,(s=e==null?void 0:e.face[0])==null?void 0:s.embedding);document.getElementById("simmilarity").innerText=`simmilarity: ${Math.trunc(1e3*t)/10}%`}var _6=performance.now();async function a1(e){let t=r1,n=document.getElementById("canvas");ae.drawFPS.push(1e3/(performance.now()-_6)),ae.drawFPS.length>ae.maxFPSframes&&ae.drawFPS.shift(),_6=performance.now(),await me.process.updateChart("FPS",ae.detectFPS),(ae.buffered||!t.canvas)&&(t.canvas=await se.image(e).canvas);let r=n.getContext("2d");r.fillStyle=ae.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),await la.face(t.face,n,ae,se.facemesh.triangulation),await la.body(t.body,n,ae),await la.hand(t.hand,n,ae),await la.gesture(t.gesture,n,ae),await Pae(t);let a=se.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*ae.detectFPS.reduce((h,d)=>h+d,0)/ae.detectFPS.length)/10,u=Math.trunc(10*ae.drawFPS.reduce((h,d)=>h+d,0)/ae.drawFPS.length)/10,c=ae.detectFPS.length>5&&l<5?'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models':"";document.getElementById("log").innerHTML=` + video: ${ae.camera.name} | facing: ${ae.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ae.camera.width} x ${ae.camera.height} ${o}
+ backend: ${se.tf.getBackend()} | ${i}
+ performance: ${Lae(t.performance)}ms FPS process:${l} refresh:${u}
${c}
- `,ie.framesDraw++,ie.lastFrame=performance.now(),ie.buffered?ie.drawThread=requestAnimationFrame(()=>r1(e,n)):!ie.buffered&&ie.drawThread&&(In("stopping buffered refresh"),cancelAnimationFrame(ie.drawThread),ie.drawThread=null)}async function a1(){var u;if(ie.busy)return null;ie.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(Xn("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=` -${a}`,In(a),Xn(a),ie.busy=!1,a;let s,i={audio:!1,video:{facingMode:ie.facing?"user":"environment",resizeMode:ie.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(c){return c.name==="PermissionDeniedError"||c.name==="NotAllowedError"?a="camera permission denied":c.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${c.message||c}`,n.innerText+=` -${a}`,Xn(a),In("camera error:",c),ie.busy=!1,a}if(s)e.srcObject=s;else return ie.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return ie.camera={name:(u=o.label)==null?void 0:u.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(c=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",ie.menuWidth.input.setAttribute("value",e.width),ie.menuHeight.input.setAttribute("value",e.height);let h=Math.trunc(window.devicePixelRatio*(8+4*t.width/window.innerWidth));ie.baseFont=ie.baseFontProto.replace(/{size}/,`${h}px`),ie.baseLineHeight=h+2,r&&e.play(),r&&!ie.detectThread&&Fc(e,t),ie.busy=!1,Xn(""),c()}})}function y6(){if(!_i){let e=null;_i=new m6(e,{trackGPU:!1,chartHz:20,chartLen:20}),_i.begin()}}function Fae(e,t,n,r){t1||(In("creating worker thread"),t1=new Worker(ie.worker,{type:"module"}),t1.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&ie.detectFPS.push(1e3/a.data.result.performance.total),ie.detectFPS.length>ie.maxFPSframes&&ie.detectFPS.shift(),ie.bench&&(_i||y6(),_i.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=ie.bench?"block":"none"),n1=a.data.result,ie.framesDetect++,ie.drawThread||r1(e),ie.detectThread=requestAnimationFrame(s=>Fc(e,n,s))})),t1.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:Ur},[t.data.buffer])}function Fc(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){ie.drawThread&&cancelAnimationFrame(ie.drawThread),ie.detectThread&&cancelAnimationFrame(ie.detectThread),ie.drawThread=null,ie.detectThread=null,e.paused?In("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>Fc(e,t),500):In(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(ie.drawThread),ie.drawThread=null,In("frame statistics: process:",ie.framesDetect,"refresh:",ie.framesDraw),In("memory",ae.tf.engine().memory());return}if(Xn(""),ie.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);Fae(e,o,t,Ur,n)}else ae.detect(e,Ur).then(s=>{s.performance&&s.performance.total&&ie.detectFPS.push(1e3/s.performance.total),ie.detectFPS.length>ie.maxFPSframes&&ie.detectFPS.shift(),ie.bench&&(_i||y6(),_i.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=ie.bench?"block":"none"),s.error?(In(s.error),document.getElementById("log").innerText+=` -Human error: ${s.error}`):(n1=s,ie.drawThread||r1(e),ie.framesDetect++,ie.detectThread=requestAnimationFrame(i=>Fc(e,t,i)))})}async function $ae(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{In("Processing image:",encodeURI(n.src));let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=ae.config.filter.width&&ae.config.filter.width>0?ae.config.filter.width:n.naturalWidth,r.height=ae.config.filter.height&&ae.config.filter.height>0?ae.config.filter.height:n.naturalHeight;let a=await ae.detect(n,Ur);n1=a,await r1(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(ie.columns+.1),s.height=s.width*r.height/r.width,a.face&&a.face.length>0?s.title=a.face.map((o,l)=>`#${l} face: ${Math.trunc(100*o.faceConfidence)}% box: ${Math.trunc(100*o.boxConfidence)}% age: ${Math.trunc(o.age)} gender: ${Math.trunc(100*o.genderConfidence)}% ${o.gender}`).join(" | "):s.title="no face detected",s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function g6(){Ur.videoOptimized=!0,document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start
video",Xn("paused"),e.pause();else{let n=await a1();if(n)Xn(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(Ae))r.hide();Xn(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause
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'),Ae.display.addBool("use 3D depth",ie,"useDepth"),Ae.display.addBool("draw boxes",ie,"drawBoxes"),Ae.display.addBool("draw polygons",ie,"drawPolygons"),Ae.display.addBool("Fill Polygons",ie,"fillPolygons"),Ae.display.addBool("draw points",ie,"drawPoints"),Ae.image=new Rc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),Ae.image.addBool("enabled",ae.config.filter,"enabled",t=>ae.config.filter.enabled=t),ie.menuWidth=Ae.image.addRange("image width",ae.config.filter,"width",0,3840,10,t=>ae.config.filter.width=parseInt(t)),ie.menuHeight=Ae.image.addRange("image height",ae.config.filter,"height",0,2160,10,t=>ae.config.filter.height=parseInt(t)),Ae.image.addHTML('
'),Ae.image.addRange("brightness",ae.config.filter,"brightness",-1,1,.05,t=>ae.config.filter.brightness=parseFloat(t)),Ae.image.addRange("contrast",ae.config.filter,"contrast",-1,1,.05,t=>ae.config.filter.contrast=parseFloat(t)),Ae.image.addRange("sharpness",ae.config.filter,"sharpness",0,1,.05,t=>ae.config.filter.sharpness=parseFloat(t)),Ae.image.addRange("blur",ae.config.filter,"blur",0,20,1,t=>ae.config.filter.blur=parseInt(t)),Ae.image.addRange("saturation",ae.config.filter,"saturation",-1,1,.05,t=>ae.config.filter.saturation=parseFloat(t)),Ae.image.addRange("hue",ae.config.filter,"hue",0,360,5,t=>ae.config.filter.hue=parseInt(t)),Ae.image.addRange("pixelate",ae.config.filter,"pixelate",0,32,1,t=>ae.config.filter.pixelate=parseInt(t)),Ae.image.addHTML('
'),Ae.image.addBool("negative",ae.config.filter,"negative",t=>ae.config.filter.negative=t),Ae.image.addBool("sepia",ae.config.filter,"sepia",t=>ae.config.filter.sepia=t),Ae.image.addBool("vintage",ae.config.filter,"vintage",t=>ae.config.filter.vintage=t),Ae.image.addBool("kodachrome",ae.config.filter,"kodachrome",t=>ae.config.filter.kodachrome=t),Ae.image.addBool("technicolor",ae.config.filter,"technicolor",t=>ae.config.filter.technicolor=t),Ae.image.addBool("polaroid",ae.config.filter,"polaroid",t=>ae.config.filter.polaroid=t),Ae.process=new Rc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),Ae.process.addList("backend",["cpu","webgl","wasm","humangl"],ae.config.backend,t=>ae.config.backend=t),Ae.process.addBool("async operations",ae.config,"async",t=>ae.config.async=t),Ae.process.addBool("enable profiler",ae.config,"profile",t=>ae.config.profile=t),Ae.process.addBool("memory shield",ae.config,"deallocate",t=>ae.config.deallocate=t),Ae.process.addBool("use web worker",ie,"useWorker"),Ae.process.addHTML('
'),Ae.process.addLabel("model parameters"),Ae.process.addRange("max objects",ae.config.face.detector,"maxFaces",1,50,1,t=>{ae.config.face.detector.maxFaces=parseInt(t),ae.config.body.maxDetections=parseInt(t),ae.config.hand.maxHands=parseInt(t)}),Ae.process.addRange("skip frames",ae.config.face.detector,"skipFrames",0,50,1,t=>{ae.config.face.detector.skipFrames=parseInt(t),ae.config.face.emotion.skipFrames=parseInt(t),ae.config.face.age.skipFrames=parseInt(t),ae.config.hand.skipFrames=parseInt(t)}),Ae.process.addRange("min confidence",ae.config.face.detector,"minConfidence",0,1,.05,t=>{ae.config.face.detector.minConfidence=parseFloat(t),ae.config.face.gender.minConfidence=parseFloat(t),ae.config.face.emotion.minConfidence=parseFloat(t),ae.config.hand.minConfidence=parseFloat(t)}),Ae.process.addRange("score threshold",ae.config.face.detector,"scoreThreshold",.1,1,.05,t=>{ae.config.face.detector.scoreThreshold=parseFloat(t),ae.config.hand.scoreThreshold=parseFloat(t),ae.config.body.scoreThreshold=parseFloat(t)}),Ae.process.addRange("overlap",ae.config.face.detector,"iouThreshold",.1,1,.05,t=>{ae.config.face.detector.iouThreshold=parseFloat(t),ae.config.hand.iouThreshold=parseFloat(t)}),Ae.process.addBool("detection rotation",ae.config.face.detector,"rotation",t=>{ae.config.face.detector.rotation=t,ae.config.hand.rotation=t}),Ae.process.addHTML('
'),Ae.process.addButton("process sample images","process images",()=>Mae()),Ae.process.addHTML('
'),Ae.process.addChart("FPS","FPS"),Ae.models=new Rc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),Ae.models.addBool("face detect",ae.config.face,"enabled",t=>ae.config.face.enabled=t),Ae.models.addBool("face mesh",ae.config.face.mesh,"enabled",t=>ae.config.face.mesh.enabled=t),Ae.models.addBool("face iris",ae.config.face.iris,"enabled",t=>ae.config.face.iris.enabled=t),Ae.models.addBool("face age",ae.config.face.age,"enabled",t=>ae.config.face.age.enabled=t),Ae.models.addBool("face gender",ae.config.face.gender,"enabled",t=>ae.config.face.gender.enabled=t),Ae.models.addBool("face emotion",ae.config.face.emotion,"enabled",t=>ae.config.face.emotion.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("body pose",ae.config.body,"enabled",t=>ae.config.body.enabled=t),Ae.models.addBool("hand pose",ae.config.hand,"enabled",t=>ae.config.hand.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("gestures",ae.config.gesture,"enabled",t=>ae.config.gesture.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("face compare",ae.config.face.embedding,"enabled",t=>{ae.config.face.embedding.enabled=t,vi=null}),document.getElementById("btnDisplay").addEventListener("click",t=>Ae.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>Ae.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>Ae.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>Ae.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>g6()),document.getElementById("play").addEventListener("click",()=>g6())}async function Oae(){if(In("Demo starting ..."),In("Browser:",navigator==null?void 0:navigator.userAgent),Dae(),document.getElementById("log").innerText=`Human: version ${ae.version}`,ie.modelsPreload&&!ie.useWorker){Xn("loading"),await ae.load(Ur);let e=Object.keys(ae.models).filter(t=>ae.models[t]);In("Demo loaded models:",e)}ie.useWorker||(Xn("initializing"),await ae.warmup(Ur)),Xn("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",In("Demo ready...")}window.onload=Oae;window.onresize=a1; + `,ae.framesDraw++,ae.lastFrame=performance.now(),ae.buffered?ae.drawThread=requestAnimationFrame(()=>a1(e,n)):!ae.buffered&&ae.drawThread&&(In("stopping buffered refresh"),cancelAnimationFrame(ae.drawThread),ae.drawThread=null)}async function s1(){var u;if(ae.busy)return null;ae.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(Kn("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=` +${a}`,In(a),Kn(a),ae.busy=!1,a;let s,i={audio:!1,video:{facingMode:ae.facing?"user":"environment",resizeMode:ae.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(c){return c.name==="PermissionDeniedError"||c.name==="NotAllowedError"?a="camera permission denied":c.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${c.message||c}`,n.innerText+=` +${a}`,Kn(a),In("camera error:",c),ae.busy=!1,a}if(s)e.srcObject=s;else return ae.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return ae.camera={name:(u=o.label)==null?void 0:u.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(c=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",ae.menuWidth.input.setAttribute("value",e.width),ae.menuHeight.input.setAttribute("value",e.height);let h=Math.trunc(window.devicePixelRatio*(8+4*t.width/window.innerWidth));ae.baseFont=ae.baseFontProto.replace(/{size}/,`${h}px`),ae.baseLineHeight=h+2,r&&e.play(),r&&!ae.detectThread&&Mc(e,t),ae.busy=!1,Kn(""),c()}})}function v6(){if(!Ii){let e=null;Ii=new b6(e,{trackGPU:!1,chartHz:20,chartLen:20}),Ii.begin()}}function Wae(e,t,n,r){n1||(In("creating worker thread"),n1=new 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All Rights Reserved. diff --git a/dist/demo-browser-index.js.map b/dist/demo-browser-index.js.map index f99c84a0..529e6bc4 100644 --- a/dist/demo-browser-index.js.map +++ b/dist/demo-browser-index.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/log.ts", "../node_modules/node-fetch/browser.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/seedrandom.js", 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"../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/imagefx.js", "../src/image.ts", "../config.js", "../src/sample.ts", "../demo/draw.js", "../demo/menu.js", "../demo/gl-bench.js", "../demo/browser.js"], - "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 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xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var 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ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var 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\"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 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y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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face.box[1], face.box[2], face.box[3]);\n }\n // silly hack since fillText does not suport new line\n const labels = [];\n labels.push(`detect confidence: ${Math.trunc(100 * face.confidence)}%`);\n if (face.genderConfidence) labels.push(`${face.gender || ''} ${Math.trunc(100 * face.genderConfidence)}% confident`);\n // if (face.genderConfidence) labels.push(face.gender);\n if (face.age) labels.push(`age: ${face.age || ''}`);\n if (face.iris) labels.push(`iris distance: ${face.iris}`);\n if (face.emotion && face.emotion.length > 0) {\n const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n labels.push(emotion.join(' '));\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = ui.baseLabel;\n for (let i = labels.length - 1; i >= 0; i--) {\n ctx.fillStyle = 'black';\n const x = Math.max(face.box[0], 0);\n const y = i * ui.baseLineHeight + face.box[1];\n ctx.fillText(labels[i], x + 5, y + 16);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(labels[i], x + 4, y + 15);\n }\n ctx.fillStyle = ui.baseColor;\n ctx.stroke();\n ctx.lineWidth = 1;\n if (face.mesh) {\n if (ui.drawPoints) {\n for (const point of face.mesh) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [\n triangulation[i * 3 + 0],\n triangulation[i * 3 + 1],\n triangulation[i * 3 + 2],\n ].map((index) => face.mesh[index]);\n const path = new Path2D();\n path.moveTo(points[0][0], points[0][1]);\n for (const point of points) {\n path.lineTo(point[0], point[1]);\n }\n path.closePath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.stroke(path);\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.fill(path);\n }\n }\n // iris: array[center, left, top, right, bottom]\n if (face.annotations && face.annotations.leftEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.leftEyeIris[4][1] - face.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.leftEyeIris[0][0], face.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n if (face.annotations && face.annotations.rightEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.rightEyeIris[0][0], face.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n }\n }\n }\n}\n\nconst lastDrawnPose = [];\nasync function drawBody(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] };\n ctx.fillStyle = ui.baseColor;\n ctx.strokeStyle = ui.baseColor;\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.beginPath();\n if (ui.buffered) {\n lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2;\n lastDrawnPose[i].keypoints[pt].position.y = (lastDrawnPose[i].keypoints[pt].position.y + result[i].keypoints[pt].position.y) / 2;\n ctx.arc(lastDrawnPose[i].keypoints[pt].position.x, lastDrawnPose[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n } else {\n ctx.arc(result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n }\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n const path = new Path2D();\n let root;\n let part;\n // torso\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg left\n root = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg right\n root = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm left\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm right\n root = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // draw all\n ctx.stroke(path);\n }\n }\n}\n\nasync function drawHand(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (const hand of result) {\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawBoxes) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.rect(hand.box[0], hand.box[1], hand.box[2], hand.box[3]);\n ctx.fillStyle = 'black';\n ctx.fillText('hand', hand.box[0] + 3, 1 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText('hand', hand.box[0] + 2, 0 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.stroke();\n }\n if (ui.drawPoints) {\n if (hand.landmarks && hand.landmarks.length > 0) {\n for (const point of hand.landmarks) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n }\n if (ui.drawPolygons) {\n const addPart = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : ui.baseColor;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n addPart(hand.annotations.indexFinger);\n addPart(hand.annotations.middleFinger);\n addPart(hand.annotations.ringFinger);\n addPart(hand.annotations.pinky);\n addPart(hand.annotations.thumb);\n // addPart(hand.annotations.palmBase);\n }\n }\n}\n\n// eslint-disable-next-line import/prefer-default-export\nexport default {\n face: drawFace,\n body: drawBody,\n hand: drawHand,\n gesture: drawGesture,\n};\n", "let instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n background: 'darkslategray',\n hover: 'lightgray',\n itemBackground: 'black',\n itemColor: 'white',\n buttonBackground: 'lightblue',\n buttonHover: 'lightgreen',\n checkboxOn: 'lightgreen',\n checkboxOff: 'lightcoral',\n rangeBackground: 'lightblue',\n rangeLabel: 'white',\n chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n if (CSScreated) return;\n const css = `\n :root { --rounded: 0.2rem; }\n .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n .menu-container { display: block; max-height: 100vh; }\n .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n .menu-title { cursor: pointer; }\n .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n .menu-label { padding: 0; font-weight: 800; }\n\n .menu-list { margin-right: 0.8rem; }\n select:focus { outline: none; }\n .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n \n .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n .menu-button:focus { outline: none; }\n\n .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n border-radius: var(--rounded); transition: left 0.6s ease; }\n\n input[type=checkbox] { visibility: hidden; }\n input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n input[type=range] { -webkit-appearance: none; }\n input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n `;\n const el = document.createElement('style');\n el.innerHTML = css;\n document.getElementsByTagName('head')[0].appendChild(el);\n CSScreated = true;\n}\n\nclass Menu {\n constructor(parent, title, position, userTheme) {\n if (userTheme) theme = { ...theme, ...userTheme };\n createCSS();\n this.createMenu(parent, title, position);\n this.id = 0;\n this.instance = instance;\n instance++;\n this._maxFPS = 0;\n this.hidden = 0;\n }\n\n createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n this.menu = document.createElement('div');\n this.menu.id = `menu-${instance}`;\n this.menu.className = 'menu';\n if (position) {\n if (position.top) this.menu.style.top = position.top;\n if (position.bottom) this.menu.style.bottom = position.bottom;\n if (position.left) this.menu.style.left = position.left;\n if (position.right) this.menu.style.right = position.right;\n }\n\n this.container = document.createElement('div');\n this.container.id = `menu-container-${instance}`;\n this.container.className = 'menu-container menu-container-fadein';\n\n // set menu title with pulldown arrow\n const elTitle = document.createElement('div');\n elTitle.className = 'menu-title';\n elTitle.id = `menu-title-${instance}`;\n const svg = `\n \n \n `;\n if (title) elTitle.innerHTML = `${title}${svg}`;\n this.menu.appendChild(elTitle);\n elTitle.addEventListener('click', () => {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n });\n\n this.menu.appendChild(this.container);\n if (typeof parent === 'object') parent.appendChild(this.menu);\n else document.getElementById(parent).appendChild(this.menu);\n }\n\n get newID() {\n this.id++;\n return `menu-${this.instance}-${this.id}`;\n }\n\n get ID() {\n return `menu-${this.instance}-${this.id}`;\n }\n\n get width() {\n return this.menu.offsetWidth;\n }\n\n get height() {\n return this.menu.offsetHeight;\n }\n\n hide() {\n if (this.container.classList.contains('menu-container-fadein')) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n }\n }\n\n visible() {\n return (this.container.classList.contains('menu-container-fadein'));\n }\n\n toggle(evt) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n if (this.container.classList.contains('menu-container-fadein') && evt) {\n const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n this.menu.style.left = null;\n this.menu.style.right = 0;\n }\n this.menu.style.borderStyle = 'solid';\n } else {\n this.menu.style.borderStyle = 'none';\n }\n }\n\n addTitle(title) {\n const el = document.createElement('div');\n el.className = 'menu-title';\n el.id = this.newID;\n el.innerHTML = title;\n this.menu.appendChild(el);\n el.addEventListener('click', () => {\n this.hidden = !this.hidden;\n const all = document.getElementsByClassName('menu');\n for (const item of all) {\n item.style.display = this.hidden ? 'none' : 'block';\n }\n });\n return el;\n }\n\n addLabel(title) {\n const el = document.createElement('div');\n el.className = 'menu-item menu-label';\n el.id = this.newID;\n el.innerHTML = title;\n this.container.appendChild(el);\n return el;\n }\n\n addBool(title, object, variable, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `
${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = evt.target.checked;\n if (callback) callback(evt.target.checked);\n });\n return el;\n }\n\n async addList(title, items, selected, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n let options = '';\n for (const item of items) {\n const def = item === selected ? 'selected' : '';\n options += ``;\n }\n el.innerHTML = `
${title}`;\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n if (callback) callback(items[evt.target.selectedIndex]);\n });\n return el;\n }\n\n addRange(title, object, variable, min, max, step, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n evt.target.setAttribute('value', evt.target.value);\n if (callback) callback(evt.target.value);\n });\n el.input = el.children[0];\n return el;\n }\n\n addHTML(html) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = this.newID;\n if (html) el.innerHTML = html;\n this.container.appendChild(el);\n return el;\n }\n\n addButton(titleOn, titleOff, callback) {\n const el = document.createElement('button');\n el.className = 'menu-item menu-button';\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n el.type = 'button';\n el.id = this.newID;\n el.innerText = titleOn;\n this.container.appendChild(el);\n el.addEventListener('click', () => {\n if (el.innerText === titleOn) el.innerText = titleOff;\n else el.innerText = titleOn;\n if (callback) callback(el.innerText !== titleOn);\n });\n return el;\n }\n\n addValue(title, val, suffix = '') {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = `menu-val-${title}`;\n el.innerText = `${title}: ${val}${suffix}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n updateValue(title, val, suffix = '') {\n const el = document.getElementById(`menu-val-${title}`);\n if (el) el.innerText = `${title}: ${val}${suffix}`;\n else this.addValue(title, val);\n }\n\n addChart(title, id, width = 150, height = 40, color) {\n if (color) theme.chartColor = color;\n const el = document.createElement('div');\n el.className = 'menu-item menu-chart-title';\n el.id = this.newID;\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n async updateChart(id, values) {\n if (!values || (values.length === 0)) return;\n const canvas = document.getElementById(`menu-canvas-${id}`);\n if (!canvas) return;\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = theme.background;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n const width = canvas.width / values.length;\n const max = 1 + Math.max(...values);\n const height = canvas.height / max;\n for (let i = 0; i < values.length; i++) {\n const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n gradient.addColorStop(0.1, theme.chartColor);\n gradient.addColorStop(0.4, theme.background);\n ctx.fillStyle = gradient;\n ctx.fillRect(i * width, 0, width - 4, canvas.height);\n ctx.fillStyle = theme.background;\n ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n }\n }\n}\n\nexport default Menu;\n", "/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 7px 0 10px; background: darkslategray; border-radius: 0.2rem; cursor: pointer; opacity: 0.9; }\n #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n #gl-bench .gl-mem { font-size: 12px; fill: white; }\n #gl-bench .gl-fps { font-size: 13px; fill: white; }\n #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n #gl-bench rect { fill: black; }\n #gl-bench .opacity { stroke: black; }\n `;\n\nconst UISVG = `\n
\n \n 00 FPS\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n `;\n\nclass GLBench {\n /** GLBench constructor\n * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n * @param { Object | undefined } settings additional settings\n */\n constructor(gl, settings = {}) {\n this.css = UICSS;\n this.svg = UISVG;\n // eslint-disable-next-line @typescript-eslint/no-empty-function\n this.paramLogger = () => {};\n // eslint-disable-next-line @typescript-eslint/no-empty-function\n this.chartLogger = () => {};\n this.chartLen = 20;\n this.chartHz = 20;\n\n this.names = [];\n this.cpuAccums = [];\n this.gpuAccums = [];\n this.activeAccums = [];\n this.chart = new Array(this.chartLen);\n this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n this.updateUI = () => {\n [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n };\n\n Object.assign(this, settings);\n this.detected = 0;\n this.finished = [];\n this.isFramebuffer = 0;\n this.frameId = 0;\n\n // 120hz device detection\n let rafId; let n = 0; let\n t0;\n const loop = (t) => {\n if (++n < 20) {\n rafId = requestAnimationFrame(loop);\n } else {\n this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n cancelAnimationFrame(rafId);\n }\n if (!t0) t0 = t;\n };\n requestAnimationFrame(loop);\n\n // attach gpu profilers\n if (gl) {\n const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n gl.getError();\n const dt = this.now() - t;\n activeAccums.forEach((active, i) => {\n if (active) this.gpuAccums[i] += dt;\n });\n }, 0));\n\n const addProfiler = (fn, self, target) => {\n const t = self.now();\n // eslint-disable-next-line prefer-rest-params\n fn.apply(target, arguments);\n if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n };\n\n /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n }\n });\n */\n const fn = 'drawElements';\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n } else {\n // eslint-disable-next-line no-console\n console.log('bench: cannot attach to webgl function');\n }\n\n /*\n gl.getExtension = ((fn, self) => {\n // eslint-disable-next-line prefer-rest-params\n const ext = fn.apply(gl, arguments);\n if (ext) {\n ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n if (ext[fn2]) {\n ext[fn2] = addProfiler(ext[fn2], self, ext);\n }\n });\n }\n return ext;\n })(gl.getExtension, this);\n */\n }\n\n // init ui and ui loggers\n if (!this.withoutUI) {\n if (!this.dom) this.dom = document.body;\n const elm = document.createElement('div');\n elm.id = 'gl-bench';\n this.dom.appendChild(elm);\n this.dom.insertAdjacentHTML('afterbegin', '');\n this.dom = elm;\n this.dom.addEventListener('click', () => {\n this.trackGPU = !this.trackGPU;\n this.updateUI();\n });\n\n this.paramLogger = ((logger, dom, names) => {\n const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n const nodes = { ...classes };\n classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n this.nodes = nodes;\n return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n // eslint-disable-next-line no-nested-ternary\n nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n };\n })(this.paramLogger, this.dom, this.names);\n\n this.chartLogger = ((logger, dom) => {\n const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n return (i, chart, circularId) => {\n let points = '';\n const len = chart.length;\n for (let j = 0; j < len; j++) {\n const id = (circularId + j + 1) % len;\n if (chart[id] !== undefined) points = points + ' ' + (55 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 22 / 60 / this.detected).toFixed(1);\n }\n nodes['gl-chart'][i].setAttribute('points', points);\n logger(this.names[i], chart, circularId);\n };\n })(this.chartLogger, this.dom);\n }\n }\n\n /**\n * Explicit UI add\n * @param { string | undefined } name\n */\n addUI(name) {\n if (this.names.indexOf(name) === -1) {\n this.names.push(name);\n if (this.dom) {\n this.dom.insertAdjacentHTML('beforeend', this.svg);\n this.updateUI();\n }\n this.cpuAccums.push(0);\n this.gpuAccums.push(0);\n this.activeAccums.push(false);\n }\n }\n\n /**\n * Increase frameID\n * @param { number | undefined } now\n */\n nextFrame(now) {\n this.frameId++;\n const t = now || this.now();\n\n // params\n if (this.frameId <= 1) {\n this.paramFrame = this.frameId;\n this.paramTime = t;\n } else {\n const duration = t - this.paramTime;\n if (duration >= 1e3) {\n const frameCount = this.frameId - this.paramFrame;\n const fps = frameCount / duration * 1e3;\n for (let i = 0; i < this.names.length; i++) {\n const cpu = this.cpuAccums[i] / duration * 100;\n const gpu = this.gpuAccums[i] / duration * 100;\n const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n this.cpuAccums[i] = 0;\n Promise.all(this.finished).then(() => {\n this.gpuAccums[i] = 0;\n this.finished = [];\n });\n }\n this.paramFrame = this.frameId;\n this.paramTime = t;\n }\n }\n\n // chart\n if (!this.detected || !this.chartFrame) {\n this.chartFrame = this.frameId;\n this.chartTime = t;\n this.circularId = 0;\n } else {\n const timespan = t - this.chartTime;\n let hz = this.chartHz * timespan / 1e3;\n while (--hz > 0 && this.detected) {\n const frameCount = this.frameId - this.chartFrame;\n const fps = frameCount / timespan * 1e3;\n this.chart[this.circularId % this.chartLen] = fps;\n for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n this.circularId++;\n this.chartFrame = this.frameId;\n this.chartTime = t;\n }\n }\n }\n\n /**\n * Begin named measurement\n * @param { string | undefined } name\n */\n begin(name) {\n this.updateAccums(name);\n }\n\n /**\n * End named measure\n * @param { string | undefined } name\n */\n end(name) {\n this.updateAccums(name);\n }\n\n updateAccums(name) {\n let nameId = this.names.indexOf(name);\n if (nameId === -1) {\n nameId = this.names.length;\n this.addUI(name);\n }\n\n const t = this.now();\n const dt = t - this.t0;\n for (let i = 0; i < nameId + 1; i++) {\n if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n }\n this.activeAccums[nameId] = !this.activeAccums[nameId];\n this.t0 = t;\n }\n}\n\nexport default GLBench;\n", "// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n// import * as tf from '@tensorflow/tfjs';\n// import Human from '../dist/human.esm-nobundle.js';\n\nimport Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human\n\nimport draw from './draw.js';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\nconst userConfig = {}; // add any user configuration overrides\n\n/*\nconst userConfig = {\n backend: 'wasm',\n async: false,\n videoOptimized: false,\n face: { enabled: true, iris: { enabled: false }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } },\n body: { enabled: false },\n hand: { enabled: false },\n};\n*/\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n baseColor: 'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n baseLabel: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n baseFontProto: 'small-caps {size} \"Segoe UI\"',\n baseLineWidth: 12,\n crop: true,\n columns: 4,\n busy: false,\n facing: true,\n useWorker: false,\n worker: 'worker.js',\n samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n compare: '../assets/sample-me.jpg',\n drawBoxes: true,\n drawPoints: false,\n drawPolygons: true,\n fillPolygons: false,\n useDepth: true,\n console: true,\n maxFPSframes: 10,\n modelsPreload: true,\n menuWidth: 0,\n menuHeight: 0,\n camera: {},\n detectFPS: [],\n drawFPS: [],\n buffered: false,\n drawThread: null,\n detectThread: null,\n framesDraw: 0,\n framesDetect: 0,\n bench: false,\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n if (!Array.isArray(msg)) return msg;\n let line = '';\n for (const entry of msg) {\n if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n else line += entry;\n }\n return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n // eslint-disable-next-line no-console\n document.getElementById('status').innerText = msg;\n}\n\nlet original;\nasync function calcSimmilariry(result) {\n document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none';\n if (!human.config.face.embedding.enabled) return;\n if ((result?.face?.length > 0) && (result?.face[0].embedding?.length !== 192)) return;\n if (!original) {\n original = result;\n document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);\n }\n const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);\n document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n const result = lastDetectedResult;\n const canvas = document.getElementById('canvas');\n\n // update draw fps data\n ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n lastDraw = performance.now();\n\n // enable for continous performance monitoring\n // console.log(result.performance);\n\n // draw fps chart\n await menu.process.updateChart('FPS', ui.detectFPS);\n\n // get updated canvas\n if (ui.buffered || !result.canvas) result.canvas = await human.image(input, userConfig);\n\n // draw image from video\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = ui.baseBackground;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n if (result.canvas) {\n if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n } else {\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n }\n\n // draw all results\n await draw.face(result.face, canvas, ui, human.facemesh.triangulation);\n await draw.body(result.body, canvas, ui);\n await draw.hand(result.hand, canvas, ui);\n await draw.gesture(result.gesture, canvas, ui);\n await calcSimmilariry(result);\n\n // update log\n const engine = human.tf.engine();\n const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? 'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models' : '';\n document.getElementById('log').innerHTML = `\n video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}
\n backend: ${human.tf.getBackend()} | ${memory}
\n performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}
\n ${warning}
\n `;\n\n ui.framesDraw++;\n ui.lastFrame = performance.now();\n // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n if (ui.buffered) {\n ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n } else if (!ui.buffered && ui.drawThread) {\n log('stopping buffered refresh');\n cancelAnimationFrame(ui.drawThread);\n ui.drawThread = null;\n }\n}\n\n// setup webcam\nasync function setupCamera() {\n if (ui.busy) return null;\n ui.busy = true;\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n const output = document.getElementById('log');\n const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n let msg = '';\n status('setting up camera');\n // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n if (!navigator.mediaDevices) {\n msg = 'camera access not supported';\n output.innerText += `\\n${msg}`;\n log(msg);\n status(msg);\n ui.busy = false;\n return msg;\n }\n let stream;\n const constraints = {\n audio: false,\n video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n };\n if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n try {\n stream = await navigator.mediaDevices.getUserMedia(constraints);\n } catch (err) {\n if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n else msg = `camera error: ${err.message || err}`;\n output.innerText += `\\n${msg}`;\n status(msg);\n log('camera error:', err);\n ui.busy = false;\n return msg;\n }\n if (stream) video.srcObject = stream;\n else {\n ui.busy = false;\n return 'camera stream empty';\n }\n const track = stream.getVideoTracks()[0];\n const settings = track.getSettings();\n // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n return new Promise((resolve) => {\n video.onloadeddata = async () => {\n video.width = video.videoWidth;\n video.height = video.videoHeight;\n canvas.width = video.width;\n canvas.height = video.height;\n canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n ui.menuWidth.input.setAttribute('value', video.width);\n ui.menuHeight.input.setAttribute('value', video.height);\n // silly font resizing for paint-on-canvas since viewport can be zoomed\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * canvas.width / window.innerWidth)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n if (live) video.play();\n // eslint-disable-next-line no-use-before-define\n if (live && !ui.detectThread) runHumanDetect(video, canvas);\n ui.busy = false;\n // do once more because onresize events can be delayed or skipped\n // if (video.width > window.innerWidth) await setupCamera();\n status('');\n resolve();\n };\n });\n}\n\nfunction initPerfMonitor() {\n if (!bench) {\n const gl = null;\n // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n // if (!gl) log('bench cannot get tensorflow webgl context');\n bench = new GLBench(gl, {\n trackGPU: false, // this is really slow\n chartHz: 20,\n chartLen: 20,\n });\n bench.begin();\n }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n if (!worker) {\n // create new webworker and add event handler only once\n log('creating worker thread');\n worker = new Worker(ui.worker, { type: 'module' });\n // after receiving message from webworker, parse&draw results and send new frame for processing\n worker.addEventListener('message', (msg) => {\n if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n lastDetectedResult = msg.data.result;\n ui.framesDetect++;\n if (!ui.drawThread) drawResults(input);\n // eslint-disable-next-line no-use-before-define\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n });\n }\n // pass image data as arraybuffer to worker by reference to avoid copy\n worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n // if live video\n const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n if (!live && input.srcObject) {\n // stop ui refresh\n if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n ui.drawThread = null;\n ui.detectThread = null;\n // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n if (input.paused) log('camera paused');\n else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n clearTimeout(ui.drawThread);\n ui.drawThread = null;\n log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n log('memory', human.tf.engine().memory());\n return;\n }\n status('');\n if (ui.useWorker) {\n // get image data from video as we cannot send html objects to webworker\n const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n offscreen.width = canvas.width;\n offscreen.height = canvas.height;\n const ctx = offscreen.getContext('2d');\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n // perform detection in worker\n webWorker(input, data, canvas, userConfig, timestamp);\n } else {\n human.detect(input, userConfig).then((result) => {\n if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n if (result.error) {\n log(result.error);\n document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n } else {\n lastDetectedResult = result;\n if (!ui.drawThread) drawResults(input);\n ui.framesDetect++;\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n }\n });\n }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n return new Promise((resolve) => {\n const image = new Image();\n image.onload = async () => {\n log('Processing image:', encodeURI(image.src));\n const canvas = document.getElementById('canvas');\n image.width = image.naturalWidth;\n image.height = image.naturalHeight;\n canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n const result = await human.detect(image, userConfig);\n lastDetectedResult = result;\n await drawResults(image);\n const thumb = document.createElement('canvas');\n thumb.className = 'thumbnail';\n thumb.width = window.innerWidth / (ui.columns + 0.1);\n thumb.height = thumb.width * canvas.height / canvas.width;\n if (result.face && result.face.length > 0) {\n thumb.title = result.face.map((a, i) => `#${i} face: ${Math.trunc(100 * a.faceConfidence)}% box: ${Math.trunc(100 * a.boxConfidence)}% age: ${Math.trunc(a.age)} gender: ${Math.trunc(100 * a.genderConfidence)}% ${a.gender}`).join(' | ');\n } else {\n thumb.title = 'no face detected';\n }\n const ctx = thumb.getContext('2d');\n ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n document.getElementById('samples-container').appendChild(thumb);\n image.src = '';\n resolve(true);\n };\n image.src = input;\n });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n userConfig.videoOptimized = true;\n document.getElementById('samples-container').style.display = 'none';\n document.getElementById('canvas').style.display = 'block';\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n if ((video.srcObject !== null) && !video.paused) {\n document.getElementById('play').style.display = 'block';\n document.getElementById('btnStart').className = 'button button-start';\n document.getElementById('btnStart').innerHTML = 'start
video';\n status('paused');\n video.pause();\n } else {\n const cameraError = await setupCamera();\n if (!cameraError) {\n document.getElementById('play').style.display = 'none';\n for (const m of Object.values(menu)) m.hide();\n status('');\n document.getElementById('btnStart').className = 'button button-stop';\n document.getElementById('btnStart').innerHTML = 'pause
video';\n await video.play();\n if (!ui.detectThread) runHumanDetect(video, canvas);\n } else {\n status(cameraError);\n }\n }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n document.getElementById('play').style.display = 'none';\n userConfig.videoOptimized = false;\n const size = Math.trunc(window.devicePixelRatio * (12 + (4 * ui.columns)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n document.getElementById('canvas').style.display = 'none';\n document.getElementById('samples-container').style.display = 'block';\n log('Running detection of sample images');\n status('processing images');\n document.getElementById('samples-container').innerHTML = '';\n for (const m of Object.values(menu)) m.hide();\n for (const image of ui.samples) await processImage(image);\n status('');\n}\n\nfunction setupMenu() {\n let x = [];\n if (window.innerWidth > 800) {\n // initial position of menu items, later it's calculated based on mouse coordinates\n x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n } else {\n // absolute minimum spacing for menus\n x = ['0rem', '11rem', '21.1rem', '33rem'];\n }\n\n menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n menu.display.addBool('crop & scale', ui, 'crop', (val) => {\n ui.crop = val;\n setupCamera();\n });\n menu.display.addBool('camera facing', ui, 'facing', (val) => {\n ui.facing = val;\n setupCamera();\n });\n menu.display.addHTML('
');\n menu.display.addBool('use 3D depth', ui, 'useDepth');\n menu.display.addBool('draw boxes', ui, 'drawBoxes');\n menu.display.addBool('draw polygons', ui, 'drawPolygons');\n menu.display.addBool('Fill Polygons', ui, 'fillPolygons');\n menu.display.addBool('draw points', ui, 'drawPoints');\n\n menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n menu.image.addBool('enabled', human.config.filter, 'enabled', (val) => human.config.filter.enabled = val);\n ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addBool('negative', human.config.filter, 'negative', (val) => human.config.filter.negative = val);\n menu.image.addBool('sepia', human.config.filter, 'sepia', (val) => human.config.filter.sepia = val);\n menu.image.addBool('vintage', human.config.filter, 'vintage', (val) => human.config.filter.vintage = val);\n menu.image.addBool('kodachrome', human.config.filter, 'kodachrome', (val) => human.config.filter.kodachrome = val);\n menu.image.addBool('technicolor', human.config.filter, 'technicolor', (val) => human.config.filter.technicolor = val);\n menu.image.addBool('polaroid', human.config.filter, 'polaroid', (val) => human.config.filter.polaroid = val);\n\n menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n menu.process.addBool('use web worker', ui, 'useWorker');\n menu.process.addHTML('
');\n menu.process.addLabel('model parameters');\n menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n human.config.face.detector.maxFaces = parseInt(val);\n human.config.body.maxDetections = parseInt(val);\n human.config.hand.maxHands = parseInt(val);\n });\n menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n human.config.face.detector.skipFrames = parseInt(val);\n human.config.face.emotion.skipFrames = parseInt(val);\n human.config.face.age.skipFrames = parseInt(val);\n human.config.hand.skipFrames = parseInt(val);\n });\n menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n human.config.face.detector.minConfidence = parseFloat(val);\n human.config.face.gender.minConfidence = parseFloat(val);\n human.config.face.emotion.minConfidence = parseFloat(val);\n human.config.hand.minConfidence = parseFloat(val);\n });\n menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.scoreThreshold = parseFloat(val);\n human.config.hand.scoreThreshold = parseFloat(val);\n human.config.body.scoreThreshold = parseFloat(val);\n });\n menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.iouThreshold = parseFloat(val);\n human.config.hand.iouThreshold = parseFloat(val);\n });\n menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n human.config.face.detector.rotation = val;\n human.config.hand.rotation = val;\n });\n menu.process.addHTML('
');\n menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n menu.process.addHTML('
');\n menu.process.addChart('FPS', 'FPS');\n\n menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n menu.models.addBool('face detect', human.config.face, 'enabled', (val) => human.config.face.enabled = val);\n menu.models.addBool('face mesh', human.config.face.mesh, 'enabled', (val) => human.config.face.mesh.enabled = val);\n menu.models.addBool('face iris', human.config.face.iris, 'enabled', (val) => human.config.face.iris.enabled = val);\n menu.models.addBool('face age', human.config.face.age, 'enabled', (val) => human.config.face.age.enabled = val);\n menu.models.addBool('face gender', human.config.face.gender, 'enabled', (val) => human.config.face.gender.enabled = val);\n menu.models.addBool('face emotion', human.config.face.emotion, 'enabled', (val) => human.config.face.emotion.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('body pose', human.config.body, 'enabled', (val) => human.config.body.enabled = val);\n menu.models.addBool('hand pose', human.config.hand, 'enabled', (val) => human.config.hand.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('gestures', human.config.gesture, 'enabled', (val) => human.config.gesture.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('face compare', human.config.face.embedding, 'enabled', (val) => {\n human.config.face.embedding.enabled = val;\n original = null;\n });\n\n document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function main() {\n log('Demo starting ...');\n log('Browser:', navigator?.userAgent);\n setupMenu();\n document.getElementById('log').innerText = `Human: version ${human.version}`;\n if (ui.modelsPreload && !ui.useWorker) {\n status('loading');\n await human.load(userConfig); // this is not required, just pre-loads all models\n const loaded = Object.keys(human.models).filter((a) => human.models[a]);\n log('Demo loaded models:', loaded);\n }\n if (!ui.useWorker) {\n status('initializing');\n await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n }\n status('human: ready');\n document.getElementById('loader').style.display = 'none';\n document.getElementById('play').style.display = 'block';\n log('Demo ready...');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"], - "mappings": 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"../demo/draw.js", "../demo/menu.js", "../demo/gl-bench.js", "../demo/browser.js"], + "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var 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asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModule) {\n WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n 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x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"async function drawPoint(canvas, x = 0, y = 0, radius = 0, color = 'black', label) {\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = color;\n ctx.beginPath();\n ctx.arc(x, y, radius, 0, 2 * Math.PI);\n ctx.fill();\n if (label) ctx.fillText(label, x + 4, y + 4);\n}\n\nasync function drawGesture(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.font = ui.baseFont;\n ctx.fillStyle = ui.baseLabel;\n let i = 1;\n for (let gesture = 0; gesture < result.length; gesture++) {\n const [where, what] = Object.entries(result[gesture]);\n if ((what.length > 1) && (what[1].length > 0)) {\n const person = where[1] > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${person}: ${what[1]}`;\n ctx.fillStyle = 'black';\n ctx.fillText(label, 8, 2 + (i * ui.baseLineHeight));\n // ctx.fillText(label, 151, i * 16 + 101);\n ctx.fillStyle = ui.baseLabel;\n // ctx.fillText(label, 150, i * 16 + 100);\n ctx.fillText(label, 6, 0 + (i * ui.baseLineHeight));\n i += 1;\n }\n }\n}\n\nasync function drawFace(result, canvas, ui, triangulation) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n for (const face of result) {\n ctx.font = ui.baseFont;\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n if (ui.drawBoxes) {\n ctx.rect(face.box[0], face.box[1], face.box[2], face.box[3]);\n }\n // silly hack since fillText does not suport new line\n const labels = [];\n labels.push(`detect confidence: ${Math.trunc(100 * face.confidence)}%`);\n if (face.genderConfidence) labels.push(`${face.gender || ''} ${Math.trunc(100 * face.genderConfidence)}% confident`);\n // if (face.genderConfidence) labels.push(face.gender);\n if (face.age) labels.push(`age: ${face.age || ''}`);\n if (face.iris) labels.push(`iris distance: ${face.iris}`);\n if (face.emotion && face.emotion.length > 0) {\n const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n labels.push(emotion.join(' '));\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = ui.baseLabel;\n for (let i = labels.length - 1; i >= 0; i--) {\n ctx.fillStyle = 'black';\n const x = Math.max(face.box[0], 0);\n const y = i * ui.baseLineHeight + face.box[1];\n ctx.fillText(labels[i], x + 5, y + 16);\n // ctx.fillText(labels[i], 151, i * 16 + 28);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(labels[i], x + 4, y + 15);\n // ctx.fillText(labels[i], 150, i * 16 + 27);\n }\n ctx.fillStyle = ui.baseColor;\n ctx.stroke();\n ctx.lineWidth = 1;\n if (face.mesh) {\n if (ui.drawPoints) {\n for (const point of face.mesh) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [\n triangulation[i * 3 + 0],\n triangulation[i * 3 + 1],\n triangulation[i * 3 + 2],\n ].map((index) => face.mesh[index]);\n const path = new Path2D();\n path.moveTo(points[0][0], points[0][1]);\n for (const point of points) {\n path.lineTo(point[0], point[1]);\n }\n path.closePath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.stroke(path);\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.fill(path);\n }\n }\n // iris: array[center, left, top, right, bottom]\n if (face.annotations && face.annotations.leftEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.leftEyeIris[4][1] - face.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.leftEyeIris[0][0], face.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n if (face.annotations && face.annotations.rightEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.rightEyeIris[0][0], face.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n }\n }\n }\n}\n\nconst lastDrawnPose = [];\nasync function drawBody(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] };\n ctx.strokeStyle = ui.baseColor;\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.fillStyle = ui.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : ui.baseColor;\n if (ui.drawLabels) {\n ctx.fillText(`${result[i].keypoints[pt].part}`, result[i].keypoints[pt].position.x + 4, result[i].keypoints[pt].position.y + 4);\n }\n ctx.beginPath();\n if (ui.buffered) {\n lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2;\n lastDrawnPose[i].keypoints[pt].position.y = (lastDrawnPose[i].keypoints[pt].position.y + result[i].keypoints[pt].position.y) / 2;\n ctx.arc(lastDrawnPose[i].keypoints[pt].position.x, lastDrawnPose[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n } else {\n ctx.arc(result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n }\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n const path = new Path2D();\n let root;\n let part;\n // torso\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg left\n root = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg right\n root = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm left\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm right\n root = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // draw all\n ctx.stroke(path);\n }\n }\n}\n\nasync function drawHand(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (const hand of result) {\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawBoxes) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.rect(hand.box[0], hand.box[1], hand.box[2], hand.box[3]);\n ctx.fillStyle = 'black';\n ctx.fillText('hand', hand.box[0] + 3, 1 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText('hand', hand.box[0] + 2, 0 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.stroke();\n }\n if (ui.drawPoints) {\n if (hand.landmarks && hand.landmarks.length > 0) {\n for (const point of hand.landmarks) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n }\n if (ui.drawPolygons) {\n const addPart = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : ui.baseColor;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n addPart(hand.annotations.indexFinger);\n addPart(hand.annotations.middleFinger);\n addPart(hand.annotations.ringFinger);\n addPart(hand.annotations.pinky);\n addPart(hand.annotations.thumb);\n // addPart(hand.annotations.palmBase);\n }\n }\n}\n\n// eslint-disable-next-line import/prefer-default-export\nexport default {\n face: drawFace,\n body: drawBody,\n hand: drawHand,\n gesture: drawGesture,\n point: drawPoint,\n};\n", "let instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n background: 'darkslategray',\n hover: 'lightgray',\n itemBackground: 'black',\n itemColor: 'white',\n buttonBackground: 'lightblue',\n buttonHover: 'lightgreen',\n checkboxOn: 'lightgreen',\n checkboxOff: 'lightcoral',\n rangeBackground: 'lightblue',\n rangeLabel: 'white',\n chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n if (CSScreated) return;\n const css = `\n :root { --rounded: 0.2rem; }\n .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n .menu-container { display: block; max-height: 100vh; }\n .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n .menu-title { cursor: pointer; }\n .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n .menu-label { padding: 0; font-weight: 800; }\n\n .menu-list { margin-right: 0.8rem; }\n select:focus { outline: none; }\n .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n \n .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n .menu-button:focus { outline: none; }\n\n .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n border-radius: var(--rounded); transition: left 0.6s ease; }\n\n input[type=checkbox] { visibility: hidden; }\n input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n input[type=range] { -webkit-appearance: none; }\n input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n `;\n const el = document.createElement('style');\n el.innerHTML = css;\n document.getElementsByTagName('head')[0].appendChild(el);\n CSScreated = true;\n}\n\nclass Menu {\n constructor(parent, title, position, userTheme) {\n if (userTheme) theme = { ...theme, ...userTheme };\n createCSS();\n this.createMenu(parent, title, position);\n this.id = 0;\n this.instance = instance;\n instance++;\n this._maxFPS = 0;\n this.hidden = 0;\n }\n\n createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n this.menu = document.createElement('div');\n this.menu.id = `menu-${instance}`;\n this.menu.className = 'menu';\n if (position) {\n if (position.top) this.menu.style.top = position.top;\n if (position.bottom) this.menu.style.bottom = position.bottom;\n if (position.left) this.menu.style.left = position.left;\n if (position.right) this.menu.style.right = position.right;\n }\n\n this.container = document.createElement('div');\n this.container.id = `menu-container-${instance}`;\n this.container.className = 'menu-container menu-container-fadein';\n\n // set menu title with pulldown arrow\n const elTitle = document.createElement('div');\n elTitle.className = 'menu-title';\n elTitle.id = `menu-title-${instance}`;\n const svg = `\n \n \n `;\n if (title) elTitle.innerHTML = `${title}${svg}`;\n this.menu.appendChild(elTitle);\n elTitle.addEventListener('click', () => {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n });\n\n this.menu.appendChild(this.container);\n if (typeof parent === 'object') parent.appendChild(this.menu);\n else document.getElementById(parent).appendChild(this.menu);\n }\n\n get newID() {\n this.id++;\n return `menu-${this.instance}-${this.id}`;\n }\n\n get ID() {\n return `menu-${this.instance}-${this.id}`;\n }\n\n get width() {\n return this.menu.offsetWidth;\n }\n\n get height() {\n return this.menu.offsetHeight;\n }\n\n hide() {\n if (this.container.classList.contains('menu-container-fadein')) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n }\n }\n\n visible() {\n return (this.container.classList.contains('menu-container-fadein'));\n }\n\n toggle(evt) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n if (this.container.classList.contains('menu-container-fadein') && evt) {\n const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n this.menu.style.left = null;\n this.menu.style.right = 0;\n }\n this.menu.style.borderStyle = 'solid';\n } else {\n this.menu.style.borderStyle = 'none';\n }\n }\n\n addTitle(title) {\n const el = document.createElement('div');\n el.className = 'menu-title';\n el.id = this.newID;\n el.innerHTML = title;\n this.menu.appendChild(el);\n el.addEventListener('click', () => {\n this.hidden = !this.hidden;\n const all = document.getElementsByClassName('menu');\n for (const item of all) {\n item.style.display = this.hidden ? 'none' : 'block';\n }\n });\n return el;\n }\n\n addLabel(title) {\n const el = document.createElement('div');\n el.className = 'menu-item menu-label';\n el.id = this.newID;\n el.innerHTML = title;\n this.container.appendChild(el);\n return el;\n }\n\n addBool(title, object, variable, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `
${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = evt.target.checked;\n if (callback) callback(evt.target.checked);\n });\n return el;\n }\n\n async addList(title, items, selected, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n let options = '';\n for (const item of items) {\n const def = item === selected ? 'selected' : '';\n options += ``;\n }\n el.innerHTML = `
${title}`;\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n if (callback) callback(items[evt.target.selectedIndex]);\n });\n return el;\n }\n\n addRange(title, object, variable, min, max, step, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n evt.target.setAttribute('value', evt.target.value);\n if (callback) callback(evt.target.value);\n });\n el.input = el.children[0];\n return el;\n }\n\n addHTML(html) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = this.newID;\n if (html) el.innerHTML = html;\n this.container.appendChild(el);\n return el;\n }\n\n addButton(titleOn, titleOff, callback) {\n const el = document.createElement('button');\n el.className = 'menu-item menu-button';\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n el.type = 'button';\n el.id = this.newID;\n el.innerText = titleOn;\n this.container.appendChild(el);\n el.addEventListener('click', () => {\n if (el.innerText === titleOn) el.innerText = titleOff;\n else el.innerText = titleOn;\n if (callback) callback(el.innerText !== titleOn);\n });\n return el;\n }\n\n addValue(title, val, suffix = '') {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = `menu-val-${title}`;\n el.innerText = `${title}: ${val}${suffix}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n updateValue(title, val, suffix = '') {\n const el = document.getElementById(`menu-val-${title}`);\n if (el) el.innerText = `${title}: ${val}${suffix}`;\n else this.addValue(title, val);\n }\n\n addChart(title, id, width = 150, height = 40, color) {\n if (color) theme.chartColor = color;\n const el = document.createElement('div');\n el.className = 'menu-item menu-chart-title';\n el.id = this.newID;\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n async updateChart(id, values) {\n if (!values || (values.length === 0)) return;\n const canvas = document.getElementById(`menu-canvas-${id}`);\n if (!canvas) return;\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = theme.background;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n const width = canvas.width / values.length;\n const max = 1 + Math.max(...values);\n const height = canvas.height / max;\n for (let i = 0; i < values.length; i++) {\n const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n gradient.addColorStop(0.1, theme.chartColor);\n gradient.addColorStop(0.4, theme.background);\n ctx.fillStyle = gradient;\n ctx.fillRect(i * width, 0, width - 4, canvas.height);\n ctx.fillStyle = theme.background;\n ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n }\n }\n}\n\nexport default Menu;\n", "/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 7px 0 10px; background: darkslategray; border-radius: 0.2rem; cursor: pointer; opacity: 0.9; }\n #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n #gl-bench .gl-mem { font-size: 12px; fill: white; }\n #gl-bench .gl-fps { font-size: 13px; fill: white; }\n #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n #gl-bench rect { fill: black; }\n #gl-bench .opacity { stroke: black; }\n `;\n\nconst UISVG = `\n
\n \n 00 FPS\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n `;\n\nclass GLBench {\n /** GLBench constructor\n * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n * @param { Object | undefined } settings additional settings\n */\n constructor(gl, settings = {}) {\n this.css = UICSS;\n this.svg = UISVG;\n // eslint-disable-next-line @typescript-eslint/no-empty-function\n this.paramLogger = () => {};\n // eslint-disable-next-line @typescript-eslint/no-empty-function\n this.chartLogger = () => {};\n this.chartLen = 20;\n this.chartHz = 20;\n\n this.names = [];\n this.cpuAccums = [];\n this.gpuAccums = [];\n this.activeAccums = [];\n this.chart = new Array(this.chartLen);\n this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n this.updateUI = () => {\n [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n };\n\n Object.assign(this, settings);\n this.detected = 0;\n this.finished = [];\n this.isFramebuffer = 0;\n this.frameId = 0;\n\n // 120hz device detection\n let rafId; let n = 0; let\n t0;\n const loop = (t) => {\n if (++n < 20) {\n rafId = requestAnimationFrame(loop);\n } else {\n this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n cancelAnimationFrame(rafId);\n }\n if (!t0) t0 = t;\n };\n requestAnimationFrame(loop);\n\n // attach gpu profilers\n if (gl) {\n const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n gl.getError();\n const dt = this.now() - t;\n activeAccums.forEach((active, i) => {\n if (active) this.gpuAccums[i] += dt;\n });\n }, 0));\n\n const addProfiler = (fn, self, target) => {\n const t = self.now();\n // eslint-disable-next-line prefer-rest-params\n fn.apply(target, arguments);\n if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n };\n\n /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n }\n });\n */\n const fn = 'drawElements';\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n } else {\n // eslint-disable-next-line no-console\n console.log('bench: cannot attach to webgl function');\n }\n\n /*\n gl.getExtension = ((fn, self) => {\n // eslint-disable-next-line prefer-rest-params\n const ext = fn.apply(gl, arguments);\n if (ext) {\n ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n if (ext[fn2]) {\n ext[fn2] = addProfiler(ext[fn2], self, ext);\n }\n });\n }\n return ext;\n })(gl.getExtension, this);\n */\n }\n\n // init ui and ui loggers\n if (!this.withoutUI) {\n if (!this.dom) this.dom = document.body;\n const elm = document.createElement('div');\n elm.id = 'gl-bench';\n this.dom.appendChild(elm);\n this.dom.insertAdjacentHTML('afterbegin', '');\n this.dom = elm;\n this.dom.addEventListener('click', () => {\n this.trackGPU = !this.trackGPU;\n this.updateUI();\n });\n\n this.paramLogger = ((logger, dom, names) => {\n const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n const nodes = { ...classes };\n classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n this.nodes = nodes;\n return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n // eslint-disable-next-line no-nested-ternary\n nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n };\n })(this.paramLogger, this.dom, this.names);\n\n this.chartLogger = ((logger, dom) => {\n const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n return (i, chart, circularId) => {\n let points = '';\n const len = chart.length;\n for (let j = 0; j < len; j++) {\n const id = (circularId + j + 1) % len;\n if (chart[id] !== undefined) points = points + ' ' + (55 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 22 / 60 / this.detected).toFixed(1);\n }\n nodes['gl-chart'][i].setAttribute('points', points);\n logger(this.names[i], chart, circularId);\n };\n })(this.chartLogger, this.dom);\n }\n }\n\n /**\n * Explicit UI add\n * @param { string | undefined } name\n */\n addUI(name) {\n if (this.names.indexOf(name) === -1) {\n this.names.push(name);\n if (this.dom) {\n this.dom.insertAdjacentHTML('beforeend', this.svg);\n this.updateUI();\n }\n this.cpuAccums.push(0);\n this.gpuAccums.push(0);\n this.activeAccums.push(false);\n }\n }\n\n /**\n * Increase frameID\n * @param { number | undefined } now\n */\n nextFrame(now) {\n this.frameId++;\n const t = now || this.now();\n\n // params\n if (this.frameId <= 1) {\n this.paramFrame = this.frameId;\n this.paramTime = t;\n } else {\n const duration = t - this.paramTime;\n if (duration >= 1e3) {\n const frameCount = this.frameId - this.paramFrame;\n const fps = frameCount / duration * 1e3;\n for (let i = 0; i < this.names.length; i++) {\n const cpu = this.cpuAccums[i] / duration * 100;\n const gpu = this.gpuAccums[i] / duration * 100;\n const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n this.cpuAccums[i] = 0;\n Promise.all(this.finished).then(() => {\n this.gpuAccums[i] = 0;\n this.finished = [];\n });\n }\n this.paramFrame = this.frameId;\n this.paramTime = t;\n }\n }\n\n // chart\n if (!this.detected || !this.chartFrame) {\n this.chartFrame = this.frameId;\n this.chartTime = t;\n this.circularId = 0;\n } else {\n const timespan = t - this.chartTime;\n let hz = this.chartHz * timespan / 1e3;\n while (--hz > 0 && this.detected) {\n const frameCount = this.frameId - this.chartFrame;\n const fps = frameCount / timespan * 1e3;\n this.chart[this.circularId % this.chartLen] = fps;\n for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n this.circularId++;\n this.chartFrame = this.frameId;\n this.chartTime = t;\n }\n }\n }\n\n /**\n * Begin named measurement\n * @param { string | undefined } name\n */\n begin(name) {\n this.updateAccums(name);\n }\n\n /**\n * End named measure\n * @param { string | undefined } name\n */\n end(name) {\n this.updateAccums(name);\n }\n\n updateAccums(name) {\n let nameId = this.names.indexOf(name);\n if (nameId === -1) {\n nameId = this.names.length;\n this.addUI(name);\n }\n\n const t = this.now();\n const dt = t - this.t0;\n for (let i = 0; i < nameId + 1; i++) {\n if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n }\n this.activeAccums[nameId] = !this.activeAccums[nameId];\n this.t0 = t;\n }\n}\n\nexport default GLBench;\n", "// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n// import * as tf from '@tensorflow/tfjs';\n// import Human from '../dist/human.esm-nobundle.js';\n\nimport Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human\n\nimport draw from './draw.js';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\nconst userConfig = {}; // add any user configuration overrides\n\n/*\nconst userConfig = {\n backend: 'wasm',\n async: false,\n warmup: 'full',\n videoOptimized: false,\n face: { enabled: true, iris: { enabled: true }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } },\n hand: { enabled: true },\n gestures: { enabled: true },\n body: { enabled: true, modelType: 'blazepose', modelPath: '../models/blazepose.json' },\n};\n*/\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n baseColor: 'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n baseLabel: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n baseFontProto: 'small-caps {size} \"Segoe UI\"',\n baseLineWidth: 12,\n crop: true,\n columns: 2,\n busy: false,\n facing: true,\n useWorker: false,\n worker: 'worker.js',\n samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n compare: '../assets/sample-me.jpg',\n drawLabels: true,\n drawBoxes: true,\n drawPoints: true,\n drawPolygons: true,\n fillPolygons: false,\n useDepth: true,\n console: true,\n maxFPSframes: 10,\n modelsPreload: true,\n menuWidth: 0,\n menuHeight: 0,\n camera: {},\n detectFPS: [],\n drawFPS: [],\n buffered: false,\n drawWarmup: false,\n drawThread: null,\n detectThread: null,\n framesDraw: 0,\n framesDetect: 0,\n bench: false,\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n if (!Array.isArray(msg)) return msg;\n let line = '';\n for (const entry of msg) {\n if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n else line += entry;\n }\n return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n // eslint-disable-next-line no-console\n document.getElementById('status').innerText = msg;\n}\n\nlet original;\nasync function calcSimmilariry(result) {\n document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none';\n if (!human.config.face.embedding.enabled) return;\n if ((result?.face?.length > 0) && (result?.face[0].embedding?.length !== 192)) return;\n if (!original) {\n original = result;\n document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);\n }\n const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);\n document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n const result = lastDetectedResult;\n const canvas = document.getElementById('canvas');\n\n // update draw fps data\n ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n lastDraw = performance.now();\n\n // enable for continous performance monitoring\n // console.log(result.performance);\n\n // draw fps chart\n await menu.process.updateChart('FPS', ui.detectFPS);\n\n // get updated canvas\n if (ui.buffered || !result.canvas) result.canvas = await human.image(input).canvas;\n\n // draw image from video\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = ui.baseBackground;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n if (result.canvas) {\n if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n } else {\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n }\n\n // draw all results\n await draw.face(result.face, canvas, ui, human.facemesh.triangulation);\n await draw.body(result.body, canvas, ui);\n await draw.hand(result.hand, canvas, ui);\n await draw.gesture(result.gesture, canvas, ui);\n await calcSimmilariry(result);\n\n // update log\n const engine = human.tf.engine();\n const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? 'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models' : '';\n document.getElementById('log').innerHTML = `\n video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}
\n backend: ${human.tf.getBackend()} | ${memory}
\n performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}
\n ${warning}
\n `;\n\n ui.framesDraw++;\n ui.lastFrame = performance.now();\n // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n if (ui.buffered) {\n ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n } else if (!ui.buffered && ui.drawThread) {\n log('stopping buffered refresh');\n cancelAnimationFrame(ui.drawThread);\n ui.drawThread = null;\n }\n}\n\n// setup webcam\nasync function setupCamera() {\n if (ui.busy) return null;\n ui.busy = true;\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n const output = document.getElementById('log');\n const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n let msg = '';\n status('setting up camera');\n // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n if (!navigator.mediaDevices) {\n msg = 'camera access not supported';\n output.innerText += `\\n${msg}`;\n log(msg);\n status(msg);\n ui.busy = false;\n return msg;\n }\n let stream;\n const constraints = {\n audio: false,\n video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n };\n if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n try {\n stream = await navigator.mediaDevices.getUserMedia(constraints);\n } catch (err) {\n if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n else msg = `camera error: ${err.message || err}`;\n output.innerText += `\\n${msg}`;\n status(msg);\n log('camera error:', err);\n ui.busy = false;\n return msg;\n }\n if (stream) video.srcObject = stream;\n else {\n ui.busy = false;\n return 'camera stream empty';\n }\n const track = stream.getVideoTracks()[0];\n const settings = track.getSettings();\n // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n return new Promise((resolve) => {\n video.onloadeddata = async () => {\n video.width = video.videoWidth;\n video.height = video.videoHeight;\n canvas.width = video.width;\n canvas.height = video.height;\n canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n ui.menuWidth.input.setAttribute('value', video.width);\n ui.menuHeight.input.setAttribute('value', video.height);\n // silly font resizing for paint-on-canvas since viewport can be zoomed\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * canvas.width / window.innerWidth)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n if (live) video.play();\n // eslint-disable-next-line no-use-before-define\n if (live && !ui.detectThread) runHumanDetect(video, canvas);\n ui.busy = false;\n // do once more because onresize events can be delayed or skipped\n // if (video.width > window.innerWidth) await setupCamera();\n status('');\n resolve();\n };\n });\n}\n\nfunction initPerfMonitor() {\n if (!bench) {\n const gl = null;\n // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n // if (!gl) log('bench cannot get tensorflow webgl context');\n bench = new GLBench(gl, {\n trackGPU: false, // this is really slow\n chartHz: 20,\n chartLen: 20,\n });\n bench.begin();\n }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n if (!worker) {\n // create new webworker and add event handler only once\n log('creating worker thread');\n worker = new Worker(ui.worker, { type: 'module' });\n // after receiving message from webworker, parse&draw results and send new frame for processing\n worker.addEventListener('message', (msg) => {\n if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n lastDetectedResult = msg.data.result;\n ui.framesDetect++;\n if (!ui.drawThread) drawResults(input);\n // eslint-disable-next-line no-use-before-define\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n });\n }\n // pass image data as arraybuffer to worker by reference to avoid copy\n worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n // if live video\n const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n if (!live && input.srcObject) {\n // stop ui refresh\n if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n ui.drawThread = null;\n ui.detectThread = null;\n // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n if (input.paused) log('camera paused');\n else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n clearTimeout(ui.drawThread);\n ui.drawThread = null;\n log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n log('memory', human.tf.engine().memory());\n return;\n }\n status('');\n if (ui.useWorker) {\n // get image data from video as we cannot send html objects to webworker\n const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n offscreen.width = canvas.width;\n offscreen.height = canvas.height;\n const ctx = offscreen.getContext('2d');\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n // perform detection in worker\n webWorker(input, data, canvas, userConfig, timestamp);\n } else {\n human.detect(input, userConfig).then((result) => {\n if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n if (result.error) {\n log(result.error);\n document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n } else {\n lastDetectedResult = result;\n if (!ui.drawThread) drawResults(input);\n ui.framesDetect++;\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n }\n });\n }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n return new Promise((resolve) => {\n const image = new Image();\n image.onload = async () => {\n log('Processing image:', encodeURI(image.src));\n const canvas = document.getElementById('canvas');\n image.width = image.naturalWidth;\n image.height = image.naturalHeight;\n canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n const result = await human.detect(image, userConfig);\n lastDetectedResult = result;\n await drawResults(image);\n const thumb = document.createElement('canvas');\n thumb.className = 'thumbnail';\n thumb.width = window.innerWidth / (ui.columns + 0.1);\n thumb.height = thumb.width * canvas.height / canvas.width;\n if (result.face && result.face.length > 0) {\n thumb.title = result.face.map((a, i) => `#${i} face: ${Math.trunc(100 * a.faceConfidence)}% box: ${Math.trunc(100 * a.boxConfidence)}% age: ${Math.trunc(a.age)} gender: ${Math.trunc(100 * a.genderConfidence)}% ${a.gender}`).join(' | ');\n } else {\n thumb.title = 'no face detected';\n }\n const ctx = thumb.getContext('2d');\n ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n document.getElementById('samples-container').appendChild(thumb);\n image.src = '';\n resolve(true);\n };\n image.src = input;\n });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n userConfig.videoOptimized = true;\n document.getElementById('samples-container').style.display = 'none';\n document.getElementById('canvas').style.display = 'block';\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n if ((video.srcObject !== null) && !video.paused) {\n document.getElementById('play').style.display = 'block';\n document.getElementById('btnStart').className = 'button button-start';\n document.getElementById('btnStart').innerHTML = 'start
video';\n status('paused');\n video.pause();\n } else {\n const cameraError = await setupCamera();\n if (!cameraError) {\n document.getElementById('play').style.display = 'none';\n for (const m of Object.values(menu)) m.hide();\n status('');\n document.getElementById('btnStart').className = 'button button-stop';\n document.getElementById('btnStart').innerHTML = 'pause
video';\n await video.play();\n if (!ui.detectThread) runHumanDetect(video, canvas);\n } else {\n status(cameraError);\n }\n }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n document.getElementById('play').style.display = 'none';\n userConfig.videoOptimized = false;\n const size = Math.trunc(window.devicePixelRatio * (12 + (4 * ui.columns)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n document.getElementById('canvas').style.display = 'none';\n document.getElementById('samples-container').style.display = 'block';\n log('Running detection of sample images');\n status('processing images');\n document.getElementById('samples-container').innerHTML = '';\n for (const m of Object.values(menu)) m.hide();\n for (const image of ui.samples) await processImage(image);\n status('');\n}\n\nfunction setupMenu() {\n let x = [];\n if (window.innerWidth > 800) {\n // initial position of menu items, later it's calculated based on mouse coordinates\n x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n } else {\n // absolute minimum spacing for menus\n x = ['0rem', '11rem', '21.1rem', '33rem'];\n }\n\n menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n menu.display.addBool('crop & scale', ui, 'crop', (val) => {\n ui.crop = val;\n setupCamera();\n });\n menu.display.addBool('camera facing', ui, 'facing', (val) => {\n ui.facing = val;\n setupCamera();\n });\n menu.display.addHTML('
');\n menu.display.addBool('use 3D depth', ui, 'useDepth');\n menu.display.addBool('print labels', ui, 'drawLabels');\n menu.display.addBool('draw boxes', ui, 'drawBoxes');\n menu.display.addBool('draw polygons', ui, 'drawPolygons');\n menu.display.addBool('Fill Polygons', ui, 'fillPolygons');\n menu.display.addBool('draw points', ui, 'drawPoints');\n\n menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n menu.image.addBool('enabled', human.config.filter, 'enabled', (val) => human.config.filter.enabled = val);\n ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addBool('negative', human.config.filter, 'negative', (val) => human.config.filter.negative = val);\n menu.image.addBool('sepia', human.config.filter, 'sepia', (val) => human.config.filter.sepia = val);\n menu.image.addBool('vintage', human.config.filter, 'vintage', (val) => human.config.filter.vintage = val);\n menu.image.addBool('kodachrome', human.config.filter, 'kodachrome', (val) => human.config.filter.kodachrome = val);\n menu.image.addBool('technicolor', human.config.filter, 'technicolor', (val) => human.config.filter.technicolor = val);\n menu.image.addBool('polaroid', human.config.filter, 'polaroid', (val) => human.config.filter.polaroid = val);\n\n menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n menu.process.addBool('use web worker', ui, 'useWorker');\n menu.process.addHTML('
');\n menu.process.addLabel('model parameters');\n menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n human.config.face.detector.maxFaces = parseInt(val);\n human.config.body.maxDetections = parseInt(val);\n human.config.hand.maxHands = parseInt(val);\n });\n menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n human.config.face.detector.skipFrames = parseInt(val);\n human.config.face.emotion.skipFrames = parseInt(val);\n human.config.face.age.skipFrames = parseInt(val);\n human.config.hand.skipFrames = parseInt(val);\n });\n menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n human.config.face.detector.minConfidence = parseFloat(val);\n human.config.face.gender.minConfidence = parseFloat(val);\n human.config.face.emotion.minConfidence = parseFloat(val);\n human.config.hand.minConfidence = parseFloat(val);\n });\n menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.scoreThreshold = parseFloat(val);\n human.config.hand.scoreThreshold = parseFloat(val);\n human.config.body.scoreThreshold = parseFloat(val);\n });\n menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.iouThreshold = parseFloat(val);\n human.config.hand.iouThreshold = parseFloat(val);\n });\n menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n human.config.face.detector.rotation = val;\n human.config.hand.rotation = val;\n });\n menu.process.addHTML('
');\n menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n menu.process.addHTML('
');\n menu.process.addChart('FPS', 'FPS');\n\n menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n menu.models.addBool('face detect', human.config.face, 'enabled', (val) => human.config.face.enabled = val);\n menu.models.addBool('face mesh', human.config.face.mesh, 'enabled', (val) => human.config.face.mesh.enabled = val);\n menu.models.addBool('face iris', human.config.face.iris, 'enabled', (val) => human.config.face.iris.enabled = val);\n menu.models.addBool('face age', human.config.face.age, 'enabled', (val) => human.config.face.age.enabled = val);\n menu.models.addBool('face gender', human.config.face.gender, 'enabled', (val) => human.config.face.gender.enabled = val);\n menu.models.addBool('face emotion', human.config.face.emotion, 'enabled', (val) => human.config.face.emotion.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('body pose', human.config.body, 'enabled', (val) => human.config.body.enabled = val);\n menu.models.addBool('hand pose', human.config.hand, 'enabled', (val) => human.config.hand.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('gestures', human.config.gesture, 'enabled', (val) => human.config.gesture.enabled = val);\n menu.models.addHTML('
');\n menu.models.addBool('face compare', human.config.face.embedding, 'enabled', (val) => {\n human.config.face.embedding.enabled = val;\n original = null;\n });\n\n document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function drawWarmup(res) {\n const canvas = document.getElementById('canvas');\n canvas.width = res.canvas.width;\n canvas.height = res.canvas.height;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(res.canvas, 0, 0, res.canvas.width, res.canvas.height, 0, 0, canvas.width, canvas.height);\n await draw.face(res.face, canvas, ui, human.facemesh.triangulation);\n await draw.body(res.body, canvas, ui);\n await draw.hand(res.hand, canvas, ui);\n await draw.gesture(res.gesture, canvas, ui);\n}\n\nasync function main() {\n log('Demo starting ...');\n log('Browser:', navigator?.userAgent);\n setupMenu();\n document.getElementById('log').innerText = `Human: version ${human.version}`;\n if (ui.modelsPreload && !ui.useWorker) {\n status('loading');\n await human.load(userConfig); // this is not required, just pre-loads all models\n const loaded = Object.keys(human.models).filter((a) => human.models[a]);\n log('Demo loaded models:', loaded);\n }\n if (!ui.useWorker) {\n status('initializing');\n const res = await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n ui.baseFont = ui.baseFontProto.replace(/{size}/, '16px');\n if (res && res.canvas && ui.drawWarmup) await drawWarmup(res);\n }\n status('human: ready');\n document.getElementById('loader').style.display = 'none';\n document.getElementById('play').style.display = 'block';\n log('Demo ready...');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"], + "mappings": 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"names": [] } diff --git a/dist/demo-browser-index.json b/dist/demo-browser-index.json index 29a0db17..21a1222c 100644 --- a/dist/demo-browser-index.json +++ b/dist/demo-browser-index.json @@ -1,11 +1,11 @@ { "inputs": { "dist/human.esm.js": { - "bytes": 1339605, + "bytes": 1341917, "imports": [] }, "demo/draw.js": { - "bytes": 10783, + "bytes": 12536, "imports": [] }, "demo/menu.js": { @@ -17,7 +17,7 @@ "imports": [] }, "demo/browser.js": { - "bytes": 27252, + "bytes": 28101, "imports": [ { "path": "dist/human.esm.js", @@ -43,7 +43,7 @@ "imports": [], "exports": [], "inputs": {}, - "bytes": 2040655 + "bytes": 2051266 }, "dist/demo-browser-index.js": { "imports": [], @@ -51,10 +51,10 @@ "entryPoint": "demo/browser.js", "inputs": { "dist/human.esm.js": { - "bytesInOutput": 1332127 + "bytesInOutput": 1334439 }, "demo/draw.js": { - "bytesInOutput": 6241 + "bytesInOutput": 7178 }, "demo/menu.js": { "bytesInOutput": 10696 @@ -63,10 +63,10 @@ "bytesInOutput": 6759 }, "demo/browser.js": { - "bytesInOutput": 17737 + "bytesInOutput": 18268 } }, - "bytes": 1380945 + "bytes": 1384725 } } } diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index 663c4c02..9667a845 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -5,14 +5,14 @@ author: ' */ -var At=Object.create,ye=Object.defineProperty,ct=Object.getPrototypeOf,_t=Object.prototype.hasOwnProperty,ht=Object.getOwnPropertyNames,xt=Object.getOwnPropertyDescriptor;var oe=t=>ye(t,"__esModule",{value:!0});var Me=(t,e)=>()=>(e||(e={exports:{}},t(e.exports,e)),e.exports),J=(t,e)=>{for(var n in e)ye(t,n,{get:e[n],enumerable:!0})},o=(t,e,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let r of ht(e))!_t.call(t,r)&&r!=="default"&&ye(t,r,{get:()=>e[r],enumerable:!(n=xt(e,r))||n.enumerable});return t},P=t=>t&&t.__esModule?t:o(oe(ye(t!=null?At(ct(t)):{},"default",{value:t,enumerable:!0})),t);import{version as yt}from"@tensorflow/tfjs/package.json";import{version as 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pt={tfjs:yt,"tfjs-core":ot,"tfjs-data":it,"tfjs-layers":at,"tfjs-converter":wt,"tfjs-backend-cpu":dt,"tfjs-backend-webgl":lt,"tfjs-backend-wasm":ft}});var U1=Me(K1=>{oe(K1);J(K1,{MediaPipeFaceMesh:()=>Le,load:()=>kt});var he=P(M()),Le=class{constructor(e,n,r,A){this.facePipeline=new Xe(e,n,r,A),this.config=A}async estimateFaces(e,n){let r=await this.facePipeline.predict(e,n),A=[];for(let c of r||[]){if(c.isDisposedInternal)continue;let _=c.coords?c.coords.arraySync():null,h=c.rawCoords,x={};if(_&&_.length>0)for(let y of Object.keys(B))x[y]=B[y].map(w=>_[w]);let 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k1=class{constructor(e,n){this.blazeFaceModel=e,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=ut(n.face.detector.inputSize),this.anchors=b.tensor2d(this.anchorsData),this.inputSize=b.tensor1d([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[n,r,A]=b.tidy(()=>{let y=e.resizeBilinear([this.width,this.height]),w=b.sub(y.div(127.5),1),l=this.blazeFaceModel.predict(w),f;if(Array.isArray(l)){let k=l.sort((d,p)=>d.size-p.size),V=b.concat([k[0],k[2]],2),q=b.concat([k[1],k[3]],2);f=b.concat([q,V],1).squeeze(0)}else f=l.squeeze();let s=gt(f,this.anchors,this.inputSize),O=b.slice(f,[0,0],[-1,1]),Z=b.sigmoid(O).squeeze();return[f,s,Z]}),c=await b.image.nonMaxSuppressionAsync(r,A,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),_=c.arraySync();c.dispose();let 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x,i=0,a;if(n.face.detector.rotation){let[m,d]=_.landmarks.length>=Et?St:Wt;i=H1(_.landmarks[m],_.landmarks[d]);let p=_e({startPoint:_.startPoint,endPoint:_.endPoint}),T=[p[0]/e.shape[2],p[1]/e.shape[1]],z=F.image.rotateWithOffset(e,i,0,T);a=je(-i,p),x=Se({startPoint:_.startPoint,endPoint:_.endPoint},z,[this.meshHeight,this.meshWidth]).div(255)}else{a=we;let m=e.clone();x=Se({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,confidence:_.confidence,image:x};let[,y,w]=this.meshDetector.predict(x),l=y.dataSync()[0];if(l_!==null),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),r=e.map(_=>_[1]),A=[Math.min(...n),Math.min(...r)],c=[Math.max(...n),Math.max(...r)];return{startPoint:A,endPoint:c,landmarks:e}}};var D0=P(U1());var Fe={};J(Fe,{FaceBoxes:()=>qe,load:()=>Vt});var ee=P(M());var He={};function K(t,e){if(!e||!e.kernels)return;let n=5,r=e.kernels.filter(h=>h.kernelTimeMs>0).reduce((h,x)=>h+=x.kernelTimeMs,0),A=e.kernels.map((h,x)=>(h.id=x,h)).filter(h=>h.kernelTimeMs>0).sort((h,x)=>x.kernelTimeMs-h.kernelTimeMs),c=e.kernels.map((h,x)=>(h.id=x,h)).filter(h=>h.totalBytesSnapshot>0).sort((h,x)=>x.totalBytesSnapshot-h.totalBytesSnapshot);A.length>n&&(A.length=n),c.length>n&&(c.length=n);let _={newBytes:e.newBytes,newTensors:e.newTensors,peakBytes:e.peakBytes,numKernelOps:e.kernels.length,timeKernelOps:r,slowestKernelOps:A,largestKernelOps:c};He[t]=_,u("Human profiler",t,_)}var qe=class{constructor(e,n){this.enlarge=1.1,this.model=e,this.config=n}async estimateFaces(e,n){n&&(this.config=n);let r=[],A=ee.image.resizeBilinear(e,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),c=A.toInt(),_,h;if(n.profile){let x=await ee.profile(()=>this.model.executeAsync(c));_=x.result[0].dataSync(),h=x.result[1].squeeze().arraySync(),x.result.forEach(a=>a.dispose()),K("faceboxes",x)}else{let[x,i,a]=await this.model.executeAsync(c);_=x.dataSync();let y=i.squeeze();h=y.arraySync(),x.dispose(),i.dispose(),y.dispose(),a.dispose()}c.dispose(),A.dispose();for(let x in h)if(_[x]&&_[x]>this.config.face.detector.minConfidence){let i=[h[x][0]/this.enlarge,h[x][1]/this.enlarge,h[x][2]*this.enlarge,h[x][3]*this.enlarge],a=[i[1],i[0],i[3]-i[1],i[2]-i[0]],y=[parseInt((a[0]*e.shape[2]).toString()),parseInt((a[1]*e.shape[1]).toString()),parseInt((a[2]*e.shape[2]).toString()),parseInt((a[3]*e.shape[1]).toString())],w=ee.image.cropAndResize(e,[i],[0],[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),l=w.div([255]);w.dispose(),r.push({confidence:_[x],box:y,boxRaw:this.config.face.mesh.returnRawData?a:null,image:l})}return r}};async function Vt(t){let e=await ee.loadGraphModel(t.face.detector.modelPath);t.debug&&u(`load model: ${t.face.detector.modelPath.match(/\/(.*)\./)[1]}`);let n=new qe(e,t);return t.face.mesh.enabled&&t.debug&&u(`load model: 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te,Ue={gender:""},me=Number.MAX_SAFE_INTEGER,Ce=!1,De=[.2989,.587,.114];async function Ge(t){return te||(te=await S.loadGraphModel(t.face.gender.modelPath),Ce=te.inputs[0].shape[3]===1,t.debug&&u(`load model: ${t.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),te}async function Qe(t,e){return te?me{let r=S.image.resizeBilinear(t,[e.face.gender.inputSize,e.face.gender.inputSize],!1),A;Ce?A=S.tidy(()=>{let[h,x,i]=S.split(r,3,3),a=S.mul(h,De[0]),y=S.mul(x,De[1]),w=S.mul(i,De[2]);return S.addN([a,y,w]).sub(.5).mul(2)}):A=S.mul(r,[255]),S.dispose(r);let c,_={gender:"",confidence:0};if(!e.profile)e.face.gender.enabled&&(c=await te.predict(A));else{let h=e.face.gender.enabled?await S.profile(()=>te.predict(A)):{};c=h.result.clone(),h.result.dispose(),K("gender",h)}if(A.dispose(),c){let h=c.dataSync();if(Ce){let x=Math.trunc(100*Math.abs(h[0]-h[1]))/100;x>e.face.gender.minConfidence&&(_.gender=h[0]>h[1]?"female":"male",_.confidence=x)}else{let 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u1=class{constructor(e,n,r){this.model=e,this.anchors=r.map(A=>[A.x_center,A.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(e){return g.tidy(()=>{let n=g.slice(e,[0,0],[-1,2]),r=g.slice(e,[0,2],[-1,2]),A=g.add(g.div(n,this.inputSizeTensor),this.anchorsTensor),c=g.div(r,this.doubleInputSizeTensor),_=g.mul(g.sub(A,c),this.inputSizeTensor),h=g.mul(g.add(A,c),this.inputSizeTensor);return g.concat2d([_,h],1)})}normalizeLandmarks(e,n){return g.tidy(()=>{let r=g.add(g.div(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return g.mul(r,this.inputSizeTensor)})}async getBoxes(e,n){let r=this.model.predict(e),A=r.squeeze();r.dispose();let c=g.tidy(()=>g.sigmoid(g.slice(A,[0,0],[-1,1])).squeeze()),_=c.dataSync(),h=g.slice(A,[0,1],[-1,4]),x=this.normalizeBoxes(h);h.dispose();let i=await 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n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return c5(n)}var m0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function Q(t,e){let n=0;for(let r=0;rg1([...c,1],n)),A=this.calculateLandmarksBoundingBox(r);return ve(Pe(A),h5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),r=ve(Pe(n),b0);r.palmLandmarks=[];for(let A=0;A[_[0]*(l[0]-this.inputSize/2),_[1]*(l[1]-this.inputSize/2),_[2]*l[2]]),x=b1(r,[0,0]),i=h.map(l=>[...g1(l,x),l[2]]),a=u0(A),y=[...xe(n),1],w=[Q(y,a[0]),Q(y,a[1])];return i.map(l=>[l[0]+w[0],l[1]+w[1],l[2]])}async estimateHands(e,n){let r=!1,A;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(A=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,A&&A.length>0&&(A.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...A],this.storedBoxes.length>0&&(r=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=ze.reshape(Z,[-1,3]),q=V.arraySync();Z.dispose(),V.dispose();let m=this.transformRawCoords(q,l,x,w),d=this.getBoxForHandLandmarks(m);this.storedBoxes[_]=d;let p={landmarks:m,confidence:k,box:{topLeft:d.startPoint,bottomRight:d.endPoint}};c.push(p)}else this.storedBoxes[_]=null;Z.dispose()}else{let x=ve(Pe(h),b0),i={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(i)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),r=e.map(_=>_[1]),A=[Math.min(...n),Math.min(...r)],c=[Math.max(...n),Math.max(...r)];return{startPoint:A,endPoint:c}}};var 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u1(e,t.hand.inputSize,T0),A=new T1(r,n,t.hand.inputSize),c=new R1(A);return t.hand.enabled&&t.debug&&u(`load model: ${t.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),t.hand.landmarks&&t.debug&&u(`load model: ${t.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),c}var v0=t=>{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),A=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&r&&A&&r.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},P0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let r=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(r)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let h=t[n].mesh[152][2];Math.abs(h)>10&&e.push({face:n,gesture:`head ${h<0?"up":"down"}`})}return e},z0=t=>{if(!t)return[];let e=[];for(let n=0;n{if(!t)return[];let e=[];for(let n=0;n0){let A=r.reduce((_,h)=>_.position[2]_.position[1](i[w]=0,y))},A=function(h,x){let i=t.createShader(x);if(t.shaderSource(i,h),t.compileShader(i),!t.getShaderParameter(i,t.COMPILE_STATUS))throw new Error("Filter: GL compile failed",t.getShaderInfoLog(i));return i};this.uniform={},this.attribute={};let 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q1=class{constructor(e,n){this.blazeFaceModel=e,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=zt(n.face.detector.inputSize),this.anchors=b.tensor2d(this.anchorsData),this.inputSize=b.tensor1d([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[n,A,r]=b.tidy(()=>{let o=e.resizeBilinear([this.width,this.height]),w=b.sub(o.div(127.5),1),l=this.blazeFaceModel.predict(w),f;if(Array.isArray(l)){let k=l.sort((d,p)=>d.size-p.size),V=b.concat([k[0],k[2]],2),J=b.concat([k[1],k[3]],2);f=b.concat([J,V],1).squeeze(0)}else f=l.squeeze();let s=Et(f,this.anchors,this.inputSize),O=b.slice(f,[0,0],[-1,1]),Z=b.sigmoid(O).squeeze();return[f,s,Z]}),c=await b.image.nonMaxSuppressionAsync(A,r,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),_=c.arraySync();c.dispose();let 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Ot=468,It=13,Zt=[It,U.midwayBetweenEyes[0]],kt=3,Vt=2,Xt=[kt,Vt],Ze=U.leftEyeLower0,ke=[Ze[0],Ze[Ze.length-1]],Ve=U.rightEyeLower0,Xe=[Ve[0],Ve[Ve.length-1]],Lt=3,Ht=4,Ft=71,Le=76;function fe(t,e,n,A=null){for(let r=0;r[_[0]*(w[0]-this.meshWidth/2),_[1]*(w[1]-this.meshHeight/2),w[2]]),x=A!==0?Ne(A,[0,0]):le,i=A!==0?h.map(w=>[...G1(w,x),w[2]]):h,a=A!==0?D1(r):le,o=[...xe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return i.map(w=>[w[0]+G(o,a[0]),w[1]+G(o,a[1]),w[2]])}getLeftToRightEyeDepthDifference(e){let n=e[ke[0]][2],A=e[Xe[0]][2];return n-A}getEyeBox(e,n,A,r,c=!1){let _=de(we(this.calculateLandmarksBoundingBox([e[A],e[r]]),this.irisEnlarge)),h=he(_),x=F.image.cropAndResize(n,[[_.startPoint[1]/this.meshHeight,_.startPoint[0]/this.meshWidth,_.endPoint[1]/this.meshHeight,_.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return c&&(x=F.image.flipLeftRight(x)),{box:_,boxSize:h,crop:x}}getEyeCoords(e,n,A,r=!1){let c=[];for(let _=0;_{let i=_;return 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re,De={gender:""},ue=Number.MAX_SAFE_INTEGER,Ge=!1,Qe=[.2989,.587,.114];async function $e(t){return re||(re=await S.loadGraphModel(t.face.gender.modelPath),Ge=re.inputs[0].shape[3]===1,t.debug&&u(`load model: ${t.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),re}async function e1(t,e){return re?ue{let A=S.image.resizeBilinear(t,[e.face.gender.inputSize,e.face.gender.inputSize],!1),r;Ge?r=S.tidy(()=>{let[h,x,i]=S.split(A,3,3),a=S.mul(h,Qe[0]),o=S.mul(x,Qe[1]),w=S.mul(i,Qe[2]);return S.addN([a,o,w]).sub(.5).mul(2)}):r=S.mul(A,[255]),S.dispose(A);let c,_={gender:"",confidence:0};if(!e.profile)e.face.gender.enabled&&(c=await re.predict(r));else{let h=e.face.gender.enabled?await S.profile(()=>re.predict(r)):{};c=h.result.clone(),h.result.dispose(),q("gender",h)}if(r.dispose(),c){let h=c.dataSync();if(Ge){let x=Math.trunc(100*Math.abs(h[0]-h[1]))/100;x>e.face.gender.minConfidence&&(_.gender=h[0]>h[1]?"female":"male",_.confidence=x)}else{let 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o1=class{constructor(e,n){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=n}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(s1(e),e);)this.exchange(e,s1(e)),e=s1(e)}sink(e){for(;2*e<=this.numberOfElements;){let n=2*e;if(ne){x=!1;break}if(!x)break}return x}function A0(t,e,n){let[A,r,c]=n.shape,_=new o1(A*r*c,({score:h})=>h);for(let h=0;hn?n:t}function _0(t,e,n,A){let r=n-t,c=A-e;return r*r+c*c}function d1(t,e){return{x:t.x+e.x,y:t.y+e.y}}var H=P(E()),ve=P(ge());function h0(t,e){let n=e.shape[0],A=new Float32Array(n);for(let 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g1=class{constructor(e,n,A){this.model=e,this.anchors=A.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(e){return g.tidy(()=>{let n=g.slice(e,[0,0],[-1,2]),A=g.slice(e,[0,2],[-1,2]),r=g.add(g.div(n,this.inputSizeTensor),this.anchorsTensor),c=g.div(A,this.doubleInputSizeTensor),_=g.mul(g.sub(r,c),this.inputSizeTensor),h=g.mul(g.add(r,c),this.inputSizeTensor);return g.concat2d([_,h],1)})}normalizeLandmarks(e,n){return g.tidy(()=>{let A=g.add(g.div(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return g.mul(A,this.inputSizeTensor)})}async getBoxes(e,n){let A=this.model.predict(e),r=A.squeeze();A.dispose();let c=g.tidy(()=>g.sigmoid(g.slice(r,[0,0],[-1,1])).squeeze()),_=c.dataSync(),h=g.slice(r,[0,1],[-1,4]),x=this.normalizeBoxes(h);h.dispose();let i=await 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n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return i5(n)}var P0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function $(t,e){let n=0;for(let A=0;Av1([...c,1],n)),r=this.calculateLandmarksBoundingBox(A);return ze(Re(r),w5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),A=ze(Re(n),E0);A.palmLandmarks=[];for(let r=0;r[_[0]*(l[0]-this.inputSize/2),_[1]*(l[1]-this.inputSize/2),_[2]*l[2]]),x=T1(A,[0,0]),i=h.map(l=>[...v1(l,x),l[2]]),a=R0(r),o=[...oe(n),1],w=[$(o,a[0]),$(o,a[1])];return i.map(l=>[l[0]+w[0],l[1]+w[1],l[2]])}async estimateHands(e,n){let A=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(A=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=Ee.reshape(Z,[-1,3]),J=V.arraySync();Z.dispose(),V.dispose();let m=this.transformRawCoords(J,l,x,w),d=this.getBoxForHandLandmarks(m);this.storedBoxes[_]=d;let p={landmarks:m,confidence:k,box:{topLeft:d.startPoint,bottomRight:d.endPoint}};c.push(p)}else this.storedBoxes[_]=null;Z.dispose()}else{let x=ze(Re(h),E0),i={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(i)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),A=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:r,endPoint:c}}};var 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ee.loadGraphModel(t.body.modelPath),Y.width=parseInt(Y.signature.inputs["input_1:0"].tensorShape.dim[2].size),Y.height=parseInt(Y.signature.inputs["input_1:0"].tensorShape.dim[1].size),t.debug&&u(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`)),Y}async function N1(t,e){if(!Y||!e.body.enabled)return null;let n={width:t.shape[2],height:t.shape[1]},A=ee.image.resizeBilinear(t,[Y.width||e.body.inputSize,Y.height||e.body.inputSize],!1),r=ee.div(A,[255]);A.dispose();let c;if(e.profile){let h=await ee.profile(()=>Y.predict(r));c=h.result.find(x=>x.size===195).dataSync(),h.result.forEach(x=>x.dispose()),q("blazepose",h)}else{let h=await Y.predict(r);c=h.find(x=>x.size===195||x.size===155).dataSync(),h.forEach(x=>x.dispose())}r.dispose();let _=[];for(let h=0;h{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),r=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&A&&r&&A.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},W0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let A=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(A)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${A<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let 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t.reduce((n,r)=>(Object.keys(r||{}).forEach(A=>{let c=n[A],_=r[A];Array.isArray(c)&&Array.isArray(_)?n[A]=c.concat(..._):e(c)&&e(_)?n[A]=se(c,_):n[A]=_}),n),{})}var G0=class{constructor(e={}){this.tf=C0,this.package=M1,this.version=S1,this.config=se(S0,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=D0,this.age=Ye,this.gender=Ke,this.emotion=$e,this.body=f1,this.hand=v1}profile(){return this.config.profile?He:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let A=n-r;A!==0&&u(...e,A)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(e,n){return this.config.face.embedding.enabled?D1(e,n):0}async load(e=null){this.state="load";let n=v();e&&(this.config=se(this.config,e)),this.firstRun&&(this.config.debug&&u(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Fe:D0;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Je(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Ge(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?n1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?A1(this.config):null),this.models.posenet||(this.config.body.enabled?p1(this.config):null),this.models.handpose||(this.config.hand.enabled?E1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Je(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Ge(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await n1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await A1(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await p1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await E1(this.config))),this.firstRun&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let A=Math.trunc(v()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let n=v();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&u("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&u("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||u("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&I1();try{await this.tf.setBackend(this.config.backend)}catch(r){u("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-n)}}async detectFace(e){var i,a,y,w,l,f;let n,r,A,c,_,h=[];this.state="run:face",n=v();let x=await((i=this.models.face)==null?void 0:i.estimateFaces(e,this.config));this.perf.face=Math.trunc(v()-n);for(let s of x){if(this.analyze("Get Face"),!s.image||s.image.isDisposedInternal){u("Face object is disposed:",s.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?Be(s.image,this.config):{}:(this.state="run:age",n=v(),r=this.config.face.age.enabled?await Be(s.image,this.config):{},this.perf.age=Math.trunc(v()-n)),this.analyze("Start Gender:"),this.config.async?A=this.config.face.gender.enabled?Qe(s.image,this.config):{}:(this.state="run:gender",n=v(),A=this.config.face.gender.enabled?await Qe(s.image,this.config):{},this.perf.gender=Math.trunc(v()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?r1(s.image,this.config):{}:(this.state="run:emotion",n=v(),c=this.config.face.emotion.enabled?await r1(s.image,this.config):{},this.perf.emotion=Math.trunc(v()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?c1(s.image,this.config):[]:(this.state="run:embedding",n=v(),_=this.config.face.embedding.enabled?await c1(s.image,this.config):[],this.perf.embedding=Math.trunc(v()-n)),this.analyze("End Emotion:"),this.config.async&&([r,A,c,_]=await Promise.all([r,A,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((a=s==null?void 0:s.annotations)==null?void 0:a.leftEyeIris)&&((y=s==null?void 0:s.annotations)==null?void 0:y.rightEyeIris)&&(delete s.annotations.leftEyeIris,delete s.annotations.rightEyeIris);let O=((w=s.annotations)==null?void 0:w.leftEyeIris)&&((l=s.annotations)==null?void 0:l.rightEyeIris)?11.7*Math.max(Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0]),Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])):0;h.push({confidence:s.confidence,faceConfidence:s.faceConfidence,boxConfidence:s.boxConfidence,box:s.box,mesh:s.mesh,boxRaw:s.boxRaw,meshRaw:s.meshRaw,annotations:s.annotations,age:r.age,gender:A.gender,genderConfidence:A.confidence,emotion:c,embedding:_,iris:O!==0?Math.trunc(O)/100:0}),(f=s.image)==null||f.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async r=>{var w,l,f,s;this.state="config";let A;this.config=se(this.config,n),this.state="check";let c=this.sanity(e);c&&(u(c,e),r({error:c}));let _,h,x,i=v();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),A=v();let a=M0(e,this.config);if(!a||!a.tensor){u("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-A),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(a.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",A=v(),x=this.config.face.enabled?await this.detectFace(a.tensor):[],this.perf.face=Math.trunc(v()-A)),this.analyze("Start Body:"),this.config.async?(_=this.config.body.enabled?(w=this.models.posenet)==null?void 0:w.estimatePoses(a.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",A=v(),_=this.config.body.enabled?await((l=this.models.posenet)==null?void 0:l.estimatePoses(a.tensor,this.config)):[],this.perf.body=Math.trunc(v()-A)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(a.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",A=v(),h=this.config.hand.enabled?await((s=this.models.handpose)==null?void 0:s.estimateHands(a.tensor,this.config)):[],this.perf.hand=Math.trunc(v()-A)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),a.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let y=[];this.config.gesture.enabled&&(A=v(),y=[...P0(x),...v0(_),...R0(h),...z0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-A)),this.perf.total=Math.trunc(v()-i),this.state="idle",r({face:x,body:_,hand:h,gesture:y,performance:this.perf,canvas:a.canvas})})}async warmupBitmap(){let e=(A,c="application/octet-stream")=>fetch(`data:${c};base64,${A}`).then(_=>_.blob()),n,r;switch(this.config.warmup){case"face":n=await e(Re);break;case"full":n=await e(Ee);break;default:n=null}if(n){let A=await createImageBitmap(n);r=await this.detect(A,this.config),A.close()}return r}async warmupCanvas(){return new Promise(e=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+Re;break;case"full":r=1200,n="data:image/jpeg;base64,"+Ee;break;default:n=null}let A=new Image(r,r);A.onload=()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");c.width=r,c.height=r;let _=c.getContext("2d");_==null||_.drawImage(A,0,0);let h=_==null?void 0:_.getImageData(0,0,r,r);this.detect(h,this.config).then(x=>e(x))},n?A.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Re):e(Ee),r=C0.node.decodeJpeg(n),A=r.expandDims(0);this.tf.dispose(r);let c=await this.detect(A,this.config);return this.tf.dispose(A),c}async warmup(e){let n=v();e&&(this.config=se(this.config,e));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let A;typeof createImageBitmap=="function"?A=await this.warmupBitmap():typeof Image!="undefined"?A=await this.warmupCanvas():A=await this.warmupNode(),this.config.videoOptimized=r;let c=v();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(c-n),"ms",A),A}};export{G0 as default}; +2Q==`;var I1={};B(I1,{author:()=>Y0,browser:()=>F0,bugs:()=>J0,default:()=>p5,dependencies:()=>D0,description:()=>V0,devDependencies:()=>Q0,engines:()=>U0,homepage:()=>B0,keywords:()=>et,license:()=>K0,main:()=>L0,module:()=>H0,name:()=>k0,peerDependencies:()=>G0,repository:()=>C0,scripts:()=>$0,sideEffects:()=>X0,types:()=>q0,version:()=>Z1});var k0="@vladmandic/human",Z1="0.40.0",V0="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",X0=!1,L0="dist/human.node.js",H0="dist/human.esm.js",F0="dist/human.esm.js",q0="types/human.d.ts",Y0="Vladimir Mandic ",J0={url:"https://github.com/vladmandic/human/issues"},B0="https://github.com/vladmandic/human#readme",K0="MIT",U0={node:">=12.0.0"},C0={type:"git",url:"git+https://github.com/vladmandic/human.git"},D0={},G0={},Q0={"@tensorflow/tfjs":"^3.2.0","@tensorflow/tfjs-backend-cpu":"^3.2.0","@tensorflow/tfjs-backend-wasm":"^3.2.0","@tensorflow/tfjs-backend-webgl":"^3.2.0","@tensorflow/tfjs-converter":"^3.2.0","@tensorflow/tfjs-core":"^3.2.0","@tensorflow/tfjs-data":"^3.2.0","@tensorflow/tfjs-layers":"^3.2.0","@tensorflow/tfjs-node":"^3.2.0","@tensorflow/tfjs-node-gpu":"^3.2.0","@types/node":"^14.14.31","@typescript-eslint/eslint-plugin":"^4.16.1","@typescript-eslint/parser":"^4.16.1","@vladmandic/pilogger":"^0.2.14",chokidar:"^3.5.1",dayjs:"^1.10.4",esbuild:"^0.8.54",eslint:"^7.21.0","eslint-config-airbnb-base":"^14.2.1","eslint-plugin-import":"^2.22.1","eslint-plugin-json":"^2.1.2","eslint-plugin-node":"^11.1.0","eslint-plugin-promise":"^4.3.1",rimraf:"^3.0.2",seedrandom:"^3.0.5","simple-git":"^2.35.2",tslib:"^2.1.0",typescript:"^4.3.0-dev.20210302"},$0={start:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",lint:"eslint src demo server",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",update:"npm update --depth 20 --force && npm dedupe && npm prune && npm audit"},et=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],p5={name:k0,version:Z1,description:V0,sideEffects:X0,main:L0,module:H0,browser:F0,types:q0,author:Y0,bugs:J0,homepage:B0,license:K0,engines:U0,repository:C0,dependencies:D0,peerDependencies:G0,devDependencies:Q0,scripts:$0,keywords:et};var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function ye(...t){let e=n=>n&&typeof n=="object";return t.reduce((n,A)=>(Object.keys(A||{}).forEach(r=>{let c=n[r],_=A[r];Array.isArray(c)&&Array.isArray(_)?n[r]=c.concat(..._):e(c)&&e(_)?n[r]=ye(c,_):n[r]=_}),n),{})}var rt=class{constructor(e={}){this.tf=tt,this.package=I1,this.version=Z1,this.config=ye(Z0,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>O1(n,this.config),this.facemesh=nt,this.age=Be,this.gender=Ce,this.emotion=t1,this.body=this.config.body.modelType.startsWith("posenet")?p1:j1,this.hand=z1}profile(){return this.config.profile?qe:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,A=this.numTensors;this.numTensors=n;let r=n-A;r!==0&&u(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(e,n){return this.config.face.embedding.enabled?n0(e,n):0}async load(e=null){this.state="load";let n=v();e&&(this.config=ye(this.config,e)),this.firstRun&&(this.config.debug&&u(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags)));let A=this.config.face.detector.modelPath.includes("faceboxes")?Ye:nt;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?A.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Ke(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?$e(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?A1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?_1(this.config):null),this.models.handpose||(this.config.hand.enabled?S1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?b1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?W1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await A.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Ke(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await $e(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await A1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await _1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await S1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await b1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await W1(this.config))),this.firstRun&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(v()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let n=v();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&u("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&u("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||u("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&H1();try{await this.tf.setBackend(this.config.backend)}catch(A){u("error: cannot set backend:",this.config.backend,A)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let A=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${A.getParameter(A.VERSION)} renderer:${A.getParameter(A.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-n)}}async detectFace(e){var i,a,o,w,l,f;let n,A,r,c,_,h=[];this.state="run:face",n=v();let x=await((i=this.models.face)==null?void 0:i.estimateFaces(e,this.config));this.perf.face=Math.trunc(v()-n);for(let s of x){if(this.analyze("Get Face"),!s.image||s.image.isDisposedInternal){u("Face object is disposed:",s.image);continue}this.analyze("Start Age:"),this.config.async?A=this.config.face.age.enabled?Ue(s.image,this.config):{}:(this.state="run:age",n=v(),A=this.config.face.age.enabled?await Ue(s.image,this.config):{},this.perf.age=Math.trunc(v()-n)),this.analyze("Start Gender:"),this.config.async?r=this.config.face.gender.enabled?e1(s.image,this.config):{}:(this.state="run:gender",n=v(),r=this.config.face.gender.enabled?await e1(s.image,this.config):{},this.perf.gender=Math.trunc(v()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?c1(s.image,this.config):{}:(this.state="run:emotion",n=v(),c=this.config.face.emotion.enabled?await c1(s.image,this.config):{},this.perf.emotion=Math.trunc(v()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?h1(s.image,this.config):[]:(this.state="run:embedding",n=v(),_=this.config.face.embedding.enabled?await h1(s.image,this.config):[],this.perf.embedding=Math.trunc(v()-n)),this.analyze("End Emotion:"),this.config.async&&([A,r,c,_]=await Promise.all([A,r,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((a=s==null?void 0:s.annotations)==null?void 0:a.leftEyeIris)&&((o=s==null?void 0:s.annotations)==null?void 0:o.rightEyeIris)&&(delete s.annotations.leftEyeIris,delete s.annotations.rightEyeIris);let O=((w=s.annotations)==null?void 0:w.leftEyeIris)&&((l=s.annotations)==null?void 0:l.rightEyeIris)?11.7*Math.max(Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0]),Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])):0;h.push({confidence:s.confidence,faceConfidence:s.faceConfidence,boxConfidence:s.boxConfidence,box:s.box,mesh:s.mesh,boxRaw:s.boxRaw,meshRaw:s.meshRaw,annotations:s.annotations,age:A.age,gender:r.gender,genderConfidence:r.confidence,emotion:c,embedding:_,iris:O!==0?Math.trunc(O)/100:0}),(f=s.image)==null||f.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async A=>{var w,l,f,s;this.state="config";let r;this.config=ye(this.config,n),this.state="check";let c=this.sanity(e);c&&(u(c,e),A({error:c}));let _,h,x,i=v();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=v();let a=O1(e,this.config);if(!a||!a.tensor){u("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-r),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(a.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=v(),x=this.config.face.enabled?await this.detectFace(a.tensor):[],this.perf.face=Math.trunc(v()-r)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?(w=this.models.posenet)==null?void 0:w.estimatePoses(a.tensor,this.config):[]:_=this.config.body.enabled?N1(a.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",r=v(),this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?await((l=this.models.posenet)==null?void 0:l.estimatePoses(a.tensor,this.config)):[]:_=this.config.body.enabled?await N1(a.tensor,this.config):[],this.perf.body=Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(a.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=v(),h=this.config.hand.enabled?await((s=this.models.handpose)==null?void 0:s.estimateHands(a.tensor,this.config)):[],this.perf.hand=Math.trunc(v()-r)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),a.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let o=[];this.config.gesture.enabled&&(r=v(),o=[...W0(x),...j0(_),...O0(h),...N0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-r)),this.perf.total=Math.trunc(v()-i),this.state="idle",A({face:x,body:_,hand:h,gesture:o,performance:this.perf,canvas:a.canvas})})}async warmupBitmap(){let e=(r,c="application/octet-stream")=>fetch(`data:${c};base64,${r}`).then(_=>_.blob()),n,A;switch(this.config.warmup){case"face":n=await e(Me);break;case"full":n=await e(Se);break;default:n=null}if(n){let r=await createImageBitmap(n);A=await this.detect(r,this.config),r.close()}return A}async warmupCanvas(){return new Promise(e=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+Me;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+Se;break;default:n=null}let r=new Image;r.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=r.naturalWidth,c.height=r.naturalHeight;let _=c.getContext("2d");_==null||_.drawImage(r,0,0);let h=await this.detect(c,this.config);e(h)},n?r.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Me):e(Se),A=tt.node.decodeJpeg(n),r=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(r,this.config);return this.tf.dispose(r),c}async warmup(e){let n=v();e&&(this.config=ye(this.config,e));let A=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=A;let c=v();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(c-n),"ms",r),r}};export{rt as default}; //# sourceMappingURL=human.esm-nobundle.js.map diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map index 831fdd97..50837d78 100644 --- a/dist/human.esm-nobundle.js.map +++ b/dist/human.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-browser.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/log.ts", "../src/human.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], - "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n 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0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n 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0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n 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0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-browser.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/log.ts", "../src/human.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], + "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 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0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||tg(t,this.size),this.strides=$i(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;rDh(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=br().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Dh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await br().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(br().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Po.print(this,e)}clone(){return this.throwIfDisposed(),Po.clone(this)}toString(e=!1){let t=this.dataSync();return ak(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Po.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),br().makeVariable(this,e,t,n)}};Object.defineProperty(Xe,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return N1("Tensor",()=>Xe)}Z();var Au=class extends Xe{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Gr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);br().disposeTensor(this),this.dataId=e.dataId,br().incRef(this,null)}dispose(){br().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Au,Symbol.hasInstance,{value:e=>e instanceof Xe&&e.assign!=null&&e.assign instanceof Function});var lr={};Oe(lr,{assertTypesMatch:()=>Ag,getTensorsInContainer:()=>C1,isTensorInList:()=>uk,makeTypesMatch:()=>gt});var R1;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(R1||(R1={}));var F1;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(F1||(F1={}));var M1;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(M1||(M1={}));var $1;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})($1||($1={}));var O1;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(O1||(O1={}));var ck={float32:$1,int32:F1,bool:M1,complex64:O1};function Yn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return ck[e][t]}function Ph(e){return Yn(e,"int32")}function gt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Yn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Ag(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function uk(e,t){return t.some(n=>n.id===e.id)}function C1(e){let t=[],n=new Set;return yg(e,t,n),t}function yg(e,t,n){if(e==null)return;if(e instanceof Xe){t.push(e);return}if(!hk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),yg(s,t,n))}}function hk(e){return Array.isArray(e)||typeof e=="object"}function D1(e){return e.kernelName!=null}var gg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},yu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new gg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){zo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof jl)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r(rthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return yu.nextTensorId++}nextVariableId(){return yu.nextVariableId++}clone(e){let t=$.runKernel(hs,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(Ja,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if($h(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=D1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(D1(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=$h(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,m,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:c}=e,h=D1(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=T1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&pa(e[0])&&(a=e.map(o=>du(o)));let s=r.write(a,t,n),i=new Xe(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=ag(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Xe(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Au(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*_1(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Au||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*_1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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a=0;a{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Xe,()=>"The result y returned by f() must be a tensor.");let s=tk(this.state.activeTape,t,a);if(!r&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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t=0;t{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Po(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof jl)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r(rthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return gu.nextTensorId++}nextVariableId(){return gu.nextVariableId++}clone(e){let t=$.runKernel(ds,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(Qa,o,l)}}),a=[];return 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o,l=z1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(z1(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=Oh(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,m,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:c}=e,h=z1(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=E1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&fa(e[0])&&(a=e.map(o=>pu(o)));let s=r.write(a,t,n),i=new Ke(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=ug(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ke(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new yu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let 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Labels / logits was rank ${t.rank} and dim was ${n}`);return Sr((r,a,s)=>{let i=Sf(a,[n],!0),o=me(fe(a,"float32"),i);s([r,o]);let l=xt(P(o,r));return{value:Ne(l,[n]),gradFunc:(u,c)=>{let[h,d]=c,p=Js(u.shape,[n]);return[P(H(u,p),me(fe(h,"float32"),Vn(d))),P(H(u,p),me(Vn(d),fe(h,"float32")))]}}})(e,t)}function GE(e,t,n,r=0,a=sn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),tn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let u=_e(r),c=_e(1),h=_e(s.shape[1]);s=se(P(s,me(c,u)),we(u,h))}let l=HE(s,i);return Yr(l,o,a)}var qE=O({softmaxCrossEntropy_:GE}),XE={fft:Du,ifft:rl,rfft:zu,irfft:fd},KE={hammingWindow:GT,hannWindow:B5,frame:V5,stft:ZT},Ke={flipLeftRight:eE,resizeNearestNeighbor:X5,resizeBilinear:q5,rotateWithOffset:nE,cropAndResize:JT,nonMaxSuppression:aE,nonMaxSuppressionAsync:dE,nonMaxSuppressionWithScore:fE,nonMaxSuppressionWithScoreAsync:AE,nonMaxSuppressionPadded:gE,nonMaxSuppressionPaddedAsync:wE},Z5={bandPart:kE,gramSchmidt:NE,qr:TE},ZE={absoluteDifference:RE,computeWeightedLoss:Yr,cosineDistance:ME,hingeLoss:OE,huberLoss:zE,logLoss:LE,meanSquaredError:BE,sigmoidCrossEntropy:jE,softmaxCrossEntropy:qE},Jr=class extends Qg{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Se(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return _5(e,t)}dispose(){this.iterations_!=null&&Se(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:_e(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Jr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var vd=class extends Jr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:B(()=>Be(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:B(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;B(()=>{let l=se(P(i,this.rho),P(st(s),1-this.rho)),u=P(we(Yt(se(o,this.epsilon)),Yt(se(i,this.epsilon))),s),c=se(P(o,this.rho),P(st(u),1-this.rho));i.assign(l),o.assign(c);let h=se(P(u,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Se(this.accumulatedGrads.map(e=>e.variable)),Se(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};vd.className="Adadelta";ba(vd);var kd=class extends Jr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:B(()=>Su(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;B(()=>{let i=se(s,st(a));s.assign(i);let o=se(P(we(a,Yt(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Se(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};kd.className="Adagrad";ba(kd);var Id=class extends Jr{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=_e(t).variable(),this.accBeta2=_e(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=me(1,this.accBeta1),r=me(1,this.accBeta2);t.forEach((a,s)=>{let 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f=se(P(we(p,se(Yt(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Se(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Se(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),B(()=>{this.accBeta1.assign(Zr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Zr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Id.className="Adam";ba(Id);var Nd=class extends Jr{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=_e(0).variable(),this.accBeta1=_e(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=me(1,this.accBeta1),r=we(-this.learningRate,se(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Nd.className="Adamax";ba(Nd);var Pu=class extends Jr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];B(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Wt(_e(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:B(()=>Be(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:B(()=>Be(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:B(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;B(()=>{let l=se(P(i,this.decay),P(st(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,c=se(P(u,this.decay),P(s,1-this.decay)),h=we(P(s,this.learningRate),Yt(me(l,se(st(c),this.epsilon)))),d=se(P(o,this.momentum),h);i.assign(l),u.assign(c),o.assign(d);let p=me(r,d);r.assign(p)}else{let u=se(P(i,this.decay),P(st(s),1-this.decay)),c=se(P(o,this.momentum),we(P(s,this.learningRate),Yt(se(u,this.epsilon))));i.assign(u),o.assign(c);let h=me(r,c);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Se(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Se(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Se(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Td.className="RMSProp";ba(Td);var ei=class{static sgd(e){return new Pu(e)}static momentum(e,t,n=!1){return new Sd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Td(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Id(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new vd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Nd(e,t,n,r,a)}static 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Sd.className="Adamax";va(Sd);var Lu=class extends Qr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];B(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Wt(_e(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=ft({inputs:{x:a},backend:n,attrs:{shape:l}}),m=nr({inputs:{x:p},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:c}}),A=ri({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var cF={kernelName:Kl,backendName:"cpu",kernelFunc:uF};function hF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Kf(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var dF={kernelName:oh,backendName:"cpu",kernelFunc:hF},pF=rt(Aa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new 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ft({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,d=Zf(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var gF={kernelName:ji,backendName:"cpu",kernelFunc:ul};function Ox(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;xe([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new 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$F(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=Pe([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=f>1?(D-E)*(h-1)/(f-1):0,j=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*j:.5*(M+L)*(d-1);if(ie<0||ie>d-1){for(let pe=0;pe1?M*(d-1)+ee*j:.5*(M+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;iey+m-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. 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vM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Ox({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Lu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=rm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var kM={kernelName:Bs,backendName:"cpu",kernelFunc:vM};function IM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Dx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Lu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=rm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var NM={kernelName:Vs,backendName:"cpu",kernelFunc:IM};function SM(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=C.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Pe([u,c],r.dtype),p=n.data.get(a.dataId).values,m=n.data.get(r.dataId).values;for(let f=0;f=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;ge>=t?1:0),FM=Vt(cs,RM,null,"bool"),MM={kernelName:cs,backendName:"cpu",kernelFunc:FM};function $M(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=ft({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Px(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var OM={kernelName:wh,backendName:"cpu",kernelFunc:$M},DM=rt(ro,e=>Number.isFinite(e)?1:0,"bool"),zM={kernelName:ro,backendName:"cpu",kernelFunc:DM},PM=rt(ao,e=>Math.abs(e)===Infinity?1:0,"bool"),LM={kernelName:ao,backendName:"cpu",kernelFunc:PM},WM=rt(so,e=>Number.isNaN(e)?1:0,"bool"),BM={kernelName:so,backendName:"cpu",kernelFunc:WM},VM=St((e,t)=>e<=t?1:0),UM=Vt(oo,VM,null,"bool"),jM={kernelName:oo,backendName:"cpu",kernelFunc:UM};function HM(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=cx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var GM={kernelName:bh,backendName:"cpu",kernelFunc:HM},qM=rt(lo,e=>Math.log1p(e)),XM={kernelName:lo,backendName:"cpu",kernelFunc:qM},KM=St((e,t)=>e&&t),ZM=Vt(uo,KM,null,"bool"),YM={kernelName:uo,backendName:"cpu",kernelFunc:ZM},JM=rt(eu,e=>e?0:1,"bool"),QM={kernelName:eu,backendName:"cpu",kernelFunc:JM},e$=St((e,t)=>e||t),t$=Vt(tu,e$,null,"bool"),n$={kernelName:tu,backendName:"cpu",kernelFunc:t$};function r$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;xe(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function m(f){let A=f%u,y=f-A+Math.max(0,A-s),g=f-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=$r({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),m=sm(d,a.shape,a.dtype,p,c,"avg");h=n.makeTensorInfo(c.outShape,a.dtype,m.values)}return h}var sF={kernelName:Ya,backendName:"cpu",kernelFunc:aF};function iF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;we(a,"avgPool3d");let c=C.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=Lx(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var oF={kernelName:Kl,backendName:"cpu",kernelFunc:iF};function lF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;we([a,s],"avgPool3DGrad");let c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,m=c.filterDepth,f=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,T=x-1-c.padInfo.left,E=w-1-c.padInfo.top,M=Pe(s.shape,"float32"),D=1/(m*f*A),L=n.bufferSync(a);for(let W=0;W=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce=c.outHeight||Math.floor(oe)!==oe))for(let pe=0;pe=c.outWidth||Math.floor(de)!==de||(te+=L.get(W,Q,oe,de,U))}}}M.set(te*D,W,H,X,G,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var uF={kernelName:oh,backendName:"cpu",kernelFunc:lF};function cF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;we([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,_=g-1-c.padInfo.left,b=y-1-c.padInfo.top,w=Pe(i.shape,"float32"),x=1/(p*m),N=n.data.get(a.dataId).values,T=Pe(a.shape,"float32",N);for(let E=0;E=c.outHeight||Math.floor(G)!==G))for(let ee=0;ee=c.outWidth||Math.floor(Y)!==Y||(H+=T.get(E,G,Y,M))}}w.set(H*x,E,D,L,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var hF={kernelName:ih,backendName:"cpu",kernelFunc:cF};function dF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=ft({inputs:{x:a},backend:n,attrs:{shape:l}}),m=rr({inputs:{x:p},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:c}}),A=ai({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var mF={kernelName:Zl,backendName:"cpu",kernelFunc:fF};function AF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Zf(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var yF={kernelName:lh,backendName:"cpu",kernelFunc:AF},gF=rt(ya,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>v.sizeFromShape(f.shape)>0);if(o.length===1)return $r({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(b=>ri({inputs:{input:b},backend:n})),A=o.map(b=>ul({inputs:{input:b},backend:n})),y=cl({inputs:f,backend:n,attrs:{axis:s}}),g=cl({inputs:A,backend:n,attrs:{axis:s}}),_=Rn({inputs:{real:y,imag:g},backend:n});return f.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),_}let u=o.map(f=>{let A=v.sizeFromShape(f.shape.slice(s));return ft({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,d=Yf(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var vF={kernelName:ji,backendName:"cpu",kernelFunc:cl};function Wx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;we([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new Ft(d.outShape,a.dtype),w=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=w[0],T=_?w[1]:w[2],E=_?w[2]:1,M=_?1:w[1],D=b.strides[0],L=_?b.strides[1]:b.strides[2],W=_?b.strides[2]:1,U=_?1:b.strides[1],H=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let pe=ce*x[0],de=Y+oe*T;for(let be=0;be=d.inWidth)continue;let Je=pe+$e*x[1],Qe=de+Fe*E,at=Je;for(let qe=0;qe=u.inDepth)continue;let ee=X*E[0],Y=D+G*T[1];for(let ae=0;ae=u.inHeight)continue;let oe=ee+Q*E[1],pe=Y+ce*T[2];for(let de=0;de=u.inWidth)continue;let Fe=oe+Re*E[2],Je=pe+$e*u.inChannels,Qe=Fe;for(let at=0;atMath.cos(e)),DF={kernelName:rs,backendName:"cpu",kernelFunc:OF},zF=rt(Gi,e=>Math.cosh(e)),PF={kernelName:Gi,backendName:"cpu",kernelFunc:zF};function LF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=Pe([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=f>1?(D-E)*(h-1)/(f-1):0,H=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*H:.5*(M+L)*(d-1);if(ie<0||ie>d-1){for(let pe=0;pe1?M*(d-1)+ee*H:.5*(M+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;iey+m-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),m=n.data.get(a.dataId).values,f=new Float32Array(o*h*d*p),A=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:A,dilationWidth:y,padInfo:g}=p,_=g.left,b=g.top,w=p.outChannels/p.inChannels,x=new Ft(p.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=x.values;for(let M=0;M=p.inHeight)continue;let ee=X*h[0],Y=D+G*c[1];for(let ae=0;ae=p.inWidth)continue;let oe=ee+Q*h[1],pe=Y+ce*p.inChannels,de=te,be=oe;for(let ke=0;ke{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:m,inWidth:f,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:b,strideWidth:w,filterHeight:x,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:M}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),D=v.sizeFromShape(M),L=M.length,W=v.getArrayFromDType(r.dtype,D);for(let U=0;U=0&&Q=0&&oeae&&(ae=be)}}}let te=v.locToIndex([U,H,G,Y],L,v.computeStrides(M));W[te]=ae}}}return{dataId:l.write(v.toTypedArray(W,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},tM={kernelName:gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${gh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let 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Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>hm(2)?2:hm(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>cw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>hw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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e[1]===t[1]&&Bd(e[0])&&Bd(t[0])}var Vd,Ud;function mw(e){if(Vd==null){let t=Or(e);Vd=t.getParameter(t.MAX_TEXTURE_SIZE)}return Vd}function fD(){Vd=null}function mD(){Ud=null}function Aw(e){if(Ud==null){let t=Or(e);Ud=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Ud)}function yw(e){if(e===0)return 0;let t,n=Or(e);return jn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:jn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function jn(e,t){return e.getExtension(t)!=null}function dm(e){try{if(Or(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function gw(e){if(e===0)return!1;let t=Or(e);if(e===1){if(!jn(t,"OES_texture_float"))return!1}else if(!jn(t,"EXT_color_buffer_float"))return!1;return mm(t)}function xw(e){if(e===0)return!1;let t=Or(e);if(e===1){if(!jn(t,"OES_texture_float")||!jn(t,"WEBGL_color_buffer_float"))return!1}else{if(jn(t,"EXT_color_buffer_float"))return mm(t);let n="EXT_color_buffer_half_float";if(jn(t,n)){let 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e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function ww(e){return e!==2?!1:Or(e).fenceSync!=null}function hl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>dm(2)?2:dm(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>mw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Aw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:yw(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Wh.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>gw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>xw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>ww(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function on(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=` bool isnan_custom(float val) { return (val > 0.0 || val < 0.0) ? false : val != 0.0; } @@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function oi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function mm(e){let t=v.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function li(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Am(e){let t=v.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}var yw=` +`}var bw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return c / 255.0; } -`,_D=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Uu.DENSE;let t=Hu(e),n=on();this.outputShape=e,this.userCode=` +`,ND=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE;let t=Gu(e),n=on();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${li(["r","c","d"],e)} return ivec3(r, c, d); } @@ -138,9 +138,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},bD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Uu.DENSE;let t=Hu(e),n=on();this.outputShape=e,this.userCode=` + `}},SD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE;let t=Gu(e),n=on();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${li(["r","c","d"],e)} return ivec3(r, c, d); } @@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},vD=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Hn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` - ${yw} + `}},TD=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Gn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` + ${bw} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},kD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Hn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` - ${yw} + `}},ED=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Gn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` + ${bw} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},ID=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=on(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` - ${mm(e)} + `}},CD=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=on(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` + ${Am(e)} void main() { ivec3 coords = getOutputCoords(); @@ -204,7 +204,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${r.output} = vec4(${i}, 0., 0., 0.); } - `}},ND=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=on(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` + `}},RD=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=on(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` localCoords = coords; if(localCoords[2] + ${u} < ${e[2]}) { localCoords[2] += ${u}; @@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee } } `}this.userCode=` - ${mm(e)} + ${Am(e)} void main() { ivec3 coords = getOutputCoords(); @@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${r.output} = ${o}; } - `}},gw={};Oe(gw,{bindVertexProgramAttributeStreams:()=>Sw,createBufferFromOutputTexture:()=>Cw,createFloat16MatrixTexture:()=>vw,createFloat16PackedMatrixTexture:()=>Nw,createFloat32MatrixTexture:()=>bw,createIndexBuffer:()=>_w,createPackedMatrixTexture:()=>Iw,createUnsignedBytesMatrixTexture:()=>kw,createVertexBuffer:()=>ww,createVertexShader:()=>xw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Fw,downloadFloat32MatrixFromBuffer:()=>Rw,downloadMatrixFromPackedOutputTexture:()=>$w,downloadPackedMatrixFromBuffer:()=>Mw,getInternalFormatForFloat16MatrixTexture:()=>ym,getInternalFormatForFloat16PackedMatrixTexture:()=>wm,getInternalFormatForFloat32MatrixTexture:()=>Am,getInternalFormatForPackedMatrixTexture:()=>xm,getInternalFormatForUnsignedBytesMatrixTexture:()=>gm,uploadDenseMatrixToTexture:()=>Tw,uploadPixelDataToTexture:()=>Ew});function xw(e){let t=on(),n=`${t.version} + `}},vw={};Oe(vw,{bindVertexProgramAttributeStreams:()=>Fw,createBufferFromOutputTexture:()=>Ow,createFloat16MatrixTexture:()=>Tw,createFloat16PackedMatrixTexture:()=>Rw,createFloat32MatrixTexture:()=>Sw,createIndexBuffer:()=>Nw,createPackedMatrixTexture:()=>Cw,createUnsignedBytesMatrixTexture:()=>Ew,createVertexBuffer:()=>Iw,createVertexShader:()=>kw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>zw,downloadFloat32MatrixFromBuffer:()=>Dw,downloadMatrixFromPackedOutputTexture:()=>Lw,downloadPackedMatrixFromBuffer:()=>Pw,getInternalFormatForFloat16MatrixTexture:()=>gm,getInternalFormatForFloat16PackedMatrixTexture:()=>_m,getInternalFormatForFloat32MatrixTexture:()=>ym,getInternalFormatForPackedMatrixTexture:()=>wm,getInternalFormatForUnsignedBytesMatrixTexture:()=>xm,uploadDenseMatrixToTexture:()=>Mw,uploadPixelDataToTexture:()=>$w});function kw(e){let t=on(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. 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Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return aw(e,t)}function Nw(e){let t=new Uint16Array([0,1,2,2,1,3]);return sw(e,t)}function qu(e,t,n,r,a,s){ow(t,n);let i=iw(e),o=e.TEXTURE_2D;return ge(e,()=>e.bindTexture(o,i)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),ge(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function ym(e){return e.internalFormatFloat}function Sw(e,t,n,r){let[a,s]=ju(t,n);return qu(e,a,s,ym(r),r.textureFormatFloat,e.FLOAT)}function gm(e){return e.internalFormatHalfFloat}function Tw(e,t,n,r){let[a,s]=ju(t,n);return qu(e,a,s,gm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function xm(e){return e.downloadTextureFormat}function Ew(e,t,n,r){let[a,s]=ju(t,n);return qu(e,a,s,xm(r),e.RGBA,e.UNSIGNED_BYTE)}function 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Uint8Array?ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Ow(e,t,n,r){let a=e.createBuffer();ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return ge(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Dw(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function zw(e,t,n,r){let[a,s]=ju(t,n),i=4,o=new Uint8Array(gD(t*n,i));return ge(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Pw(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(xD(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Lw(e,t,n){let r=new Float32Array(t*n*4);return ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Hd=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Wd(t,e)):this.gl=Or(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Bu(this.gl,a),jn(this.gl,s))this.textureHalfFloatExtension=Bu(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),jn(this.gl,r))this.colorBufferHalfFloatExtension=Bu(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",jn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(jn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Iw(this.gl),this.indexBuffer=Nw(this.gl),this.framebuffer=lw(this.gl),this.textureConfig=fm(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ge(e,()=>e.finish()),ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.deleteFramebuffer(this.framebuffer)),ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ge(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Sw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Tw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Ew(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),$w(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Mw(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Rw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(hm(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>zw(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Pw(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Dw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Ow(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Lw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=tw(t,e),r=kw(t),a=nw(t);return ge(t,()=>t.attachShader(a,r)),ge(t,()=>t.attachShader(a,n)),rw(t,a),this.debug&&zd(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Fw(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ge(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&zd(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?cw(this.gl,e,t):hw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ge(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),dw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=dl(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&zd(this.gl,this.program),Vu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ge(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ge(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Bu(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=FD(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Pd(this.gl,e,this.framebuffer),this.debug&&Vu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Pd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Vu(this.gl)):hm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Pd(r,e,this.framebuffer),this.debug&&Vu(r),this.outputTexture=e,ge(r,()=>r.viewport(0,0,t,n)),ge(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ge(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function FD(e){let t=0;for(;t{let m=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${m>1?`[${m}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(` +`),i=e.map(p=>MD(p,t,r)).join(` +`),o=t.texShape,l=on(),u=DD(l),c,h,d=LD(l);return t.isPacked?(c=$D(t.logicalShape,o),h=PD(l)):(c=OD(t.logicalShape,o),h=zD(l)),r&&(d+=WD),[d,u,h,s,c,i,n].join(` +`)}function pl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return VD(e);case 1:return UD(e);case 2:return HD(e);case 3:return jD(e);case 4:return GD(e);case 5:return qD(e);case 6:return XD(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Bw(e){switch(e.shapeInfo.logicalShape.length){case 0:return KD(e);case 1:return ZD(e);case 2:return YD(e);case 3:return JD(e);default:return QD(e)}}function MD(e,t,n=!1){let r="";n?r+=Bw(e):r+=pl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=ez(e,t):r+=tz(e,t)),r}function $D(e,t){switch(e.length){case 0:return Vw();case 1:return nz(e,t);case 2:return sz(e,t);case 3:return rz(e,t);default:return az(e,t)}}function OD(e,t){switch(e.length){case 0:return Vw();case 1:return iz(e,t);case 2:return hz(e,t);case 3:return oz(e,t);case 4:return lz(e,t);case 5:return uz(e,t);case 6:return cz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function DD(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function FD(e){return` + `}function zD(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function MD(e){return` + `}function PD(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function $D(e){return`${e.version} + `}function LD(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return fract((p3.x + p3.y) * p3.z); } - ${oz} - ${lz} - ${uz} - `}var oz=` + ${dz} + ${pz} + ${fz} + `}var dz=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,lz=` +`,pz=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,uz=` +`,fz=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,OD=` +`,WD=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function zw(){return` +`;function Vw(){return` int getOutputCoords() { return 0; } - `}function YD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?` + `}function nz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?` int getOutputCoords() { return 2 * int(resultUV.x * ${n[1]}.0); } @@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function tz(e,t){return t[0]===1?` + `}function iz(e,t){return t[0]===1?` int getOutputCoords() { return int(resultUV.x * ${t[1]}.0); } @@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function JD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return` + `}function rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]})); @@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function nz(e,t){let n=oi(["r","c","d"],e);return` + `}function oz(e,t){let n=li(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function QD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(` + `}function ez(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Ww(e.shapeInfo.logicalShape,t.logicalShape),l=it(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(` `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)p=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?p=` @@ -804,22 +804,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${r}(${d}); ${p} } - `}function ZD(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` + `}function tz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` float ${a}() { return sampleTexture(${n}, resultUV); } - `;let u=it(l),c=Ow(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(f=>`coords.${p[f+h]} = 0;`).join(` + `;let u=it(l),c=Ww(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(f=>`coords.${p[f+h]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${p[A+h]}`).join(", "),` float ${a}() { ${u} coords = getOutputCoords(); ${d} return get${r}(${m}); } - `}function it(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function fl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ml(e,t){return t.map(n=>e[n]).join(", ")}function cz(e,t,n,r){let a=t.userCode,s=n.map((p,m)=>{let f={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(f.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=DD(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. 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`}function qz(e,t){let n=e.length,r=Kz(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${r[0]}), cEdge ? 0. : getA(${r[1]}), rEdge ? 0. : getA(${r[2]}), - rEdge || cEdge ? 0. : getA(${r[3]})`}var Vw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${r[3]})`}var qw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=` ${a} ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${r>0?"}":""} `}this.userCode=` - ${Hz(t)} - 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r===Jt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Jt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Jt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Kw(n,r),s=Zw(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Xw(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Jz(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Xw(e,t,n,r,a){let s=Qz(t,r),i;if(a){let[l,u]=dl(e[0],e[1]);i=l*u}else{let[l,u]=ju(e[0],e[1]);i=l*u}let o=Jz(n,s);return i*o}function Qz(e,t){switch(e){case Jt.PACKED_2X2_FLOAT32:return wm(t);case Jt.PACKED_2X2_FLOAT16:return _m(t);case Jt.UNPACKED_FLOAT32:return ym(t);case Jt.UNPACKED_FLOAT16:return gm(t);case Jt.PACKED_4X1_UNSIGNED_BYTE:return xm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Jt.PACKED_2X2_FLOAT32:Jt.UNPACKED_FLOAT32:e?Jt.PACKED_2X2_FLOAT16:Jt.UNPACKED_FLOAT16}function Kw(e,t){if(e===Gn.UPLOAD)return Jt.PACKED_2X2_FLOAT32;if(e===Gn.RENDER||e==null)return eP(t);if(e===Gn.DOWNLOAD||e===Gn.PIXELS)return Jt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Zw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ra=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=` float unaryOperation(float x) { ${t} } @@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},hr="if (isnan(x)) return x;",Zz="return x;",Gw="return abs(x);",Yz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Jz=hr+` + `}},fr="if (isnan(x)) return x;",tP="return x;",Yw="return abs(x);",nP="return (x >= 0.0) ? x : (exp(x) - 1.0);",rP=fr+` return (x < 0.0) ? 0.0 : x; -`,Qz=hr+` +`,aP=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,jd="return x;",eP="return x;",tP=` +`,jd="return x;",sP="return x;",iP=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,nP=` +`,oP=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,rP=` +`,lP=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Al=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` +`,yl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},aP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=ln("rc",t),r=it(t),a=Lz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},uP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=ln("rc",t),r=it(t),a=Hz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${r} rc = getOutputCoords(); vec4 packedInput = getA(${a}); setOutput(getChannel(packedInput, ${i})); } - `}},sP=Rr.whereImpl,iP=1e-7,oP=1e-4,bm={};function lP(e){return e in bm||(bm[e]={}),bm[e]}var uP=128,cP=600;function hP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*cP/1024/1024}var yl=class extends jl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Mr(J().getNumber("WEBGL_VERSION"));this.binaryCache=lP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Ud(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Gz(this.gpgpu),this.numMBBeforeWarning=hP(),this.texData=new Qc(this,Ir())}nextDataId(){return yl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Hn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Hn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Al(i,jd):h=new Ca(i,jd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(m=>p.push(m))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Al(r,jd):p=new Ca(r,jd);let m=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Hu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=p[0],f=p[1];c=C.mergeRealAndImagArrays(m,f)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ir().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ir().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=uP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Ir().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new aP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Uz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ai(e.shape),...si(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[ai(t),...si(t)],s=new Vw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Pd(r),i;n?i=new bD(s):i=new _D(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Uu.DENSE){let f=Hu(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Vu(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=dz(e,l,u),h=this.getAndSaveBinary(c,()=>cz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),hz(this.gpgpu,h,l,u,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=v.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(_e(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?iP:oP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=uw(n,o),t.texShape=c),a!=null){let h=Pd(n),d,p=c[1],m=c[0],f=a instanceof Uint8Array;o?([p,m]=hl(c[0],c[1]),d=new ND(h,[m,p],f)):d=new ID(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Hn.PIXELS:this.texData.get(A.dataId).usage=Hn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=dP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};yl.nextDataId=0;function dP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew yl,2);var pP={forceHalfFloat:Xw},Kw=` + `}},cP=Mr.whereImpl,hP=1e-7,dP=1e-4,vm={};function pP(e){return e in vm||(vm[e]={}),vm[e]}var fP=128,mP=600;function AP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*mP/1024/1024}var gl=class extends jl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Or(J().getNumber("WEBGL_VERSION"));this.binaryCache=pP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Hd(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Yz(this.gpgpu),this.numMBBeforeWarning=AP(),this.texData=new eh(this,Sr())}nextDataId(){return gl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Gn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Gn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new yl(i,jd):h=new Ra(i,jd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(m=>p.push(m))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new yl(r,jd):p=new Ra(r,jd);let m=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Gu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=p[0],f=p[1];c=C.mergeRealAndImagArrays(m,f)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Sr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Sr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=fP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Sr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new uP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[si(e.shape),...ii(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[si(t),...ii(t)],s=new qw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Ld(r),i;n?i=new SD(s):i=new ND(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Hu.DENSE){let f=Gu(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Uu(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=yz(e,l,u),h=this.getAndSaveBinary(c,()=>mz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Az(this.gpgpu,h,l,u,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=v.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(_e(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hP:dP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=fw(n,o),t.texShape=c),a!=null){let h=Ld(n),d,p=c[1],m=c[0],f=a instanceof Uint8Array;o?([p,m]=dl(c[0],c[1]),d=new RD(h,[m,p],f)):d=new CD(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Gn.PIXELS:this.texData.get(A.dataId).usage=Gn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=yP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};gl.nextDataId=0;function yP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew gl,2);var gP={forceHalfFloat:Qw},e_=` if (isnan(a)) return a; if (isnan(b)) return b; -`,gl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` +`,xl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -952,12 +952,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},Hd=` + `}},Gd=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`,qu=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` +`,Xu=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Fn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var fP={kernelName:hs,backendName:"webgl",kernelFunc:Fn};function Ra(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Fn({inputs:{x:r},backend:n}),l=Fn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var mP={kernelName:lh,backendName:"webgl",kernelFunc:Ra},Zw="return (a < 0.) ? b * a : a;",Yw=` + `}};function Fn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var xP={kernelName:ds,backendName:"webgl",kernelFunc:Fn};function Fa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Fn({inputs:{x:r},backend:n}),l=Fn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var wP={kernelName:uh,backendName:"webgl",kernelFunc:Fa},t_="return (a < 0.) ? b * a : a;",n_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function AP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(Yw,a.shape,i.shape):new gl(Zw,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var yP={kernelName:ds,backendName:"webgl",kernelFunc:AP},Jw="return (a < 0.) ? b * a : a;",Qw=` +`;function _P(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(n_,a.shape,i.shape):new xl(t_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var bP={kernelName:ps,backendName:"webgl",kernelFunc:_P},r_="return (a < 0.) ? b * a : a;",a_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function gP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(Qw,r.shape,a.shape):new gl(Jw,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var xP={kernelName:ks,backendName:"webgl",kernelFunc:gP},e_="if (isnan(x)) return x;",wP=` +`;function vP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(a_,r.shape,a.shape):new xl(r_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var kP={kernelName:Is,backendName:"webgl",kernelFunc:vP},s_="if (isnan(x)) return x;",IP=` if (isnan(a)) return a; if (isnan(b)) return b; -`,_P=` +`,NP=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`;function He({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Al(i.shape,t):c=new Ca(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},T=new gl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Yn(b.dtype,w.dtype))}),g=Ra({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Yn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,m.values,f.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new qu(t,l.shape,u.shape,n):p=new gl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Gd(e,t=!1){if(e==="linear")return t?eP:Zz;if(e==="relu")return t?nP:Jz;if(e==="elu")return t?tP:Yz;if(e==="relu6")return t?rP:Qz;if(e==="prelu")return t?Qw:Jw;if(e==="leakyrelu")return t?Yw:Zw;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var t_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) { +`;function Ge({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new yl(i.shape,t):c=new Ra(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},T=new xl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Jn(b.dtype,w.dtype))}),g=Fa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Jn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,m.values,f.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new Xu(t,l.shape,u.shape,n):p=new xl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function qd(e,t=!1){if(e==="linear")return t?sP:tP;if(e==="relu")return t?oP:rP;if(e==="elu")return t?iP:nP;if(e==="relu6")return t?lP:aP;if(e==="prelu")return t?a_:r_;if(e==="leakyrelu")return t?n_:t_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var i_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},n_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},r_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` + `}},o_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},l_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},a_="return a * b;";function s_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new r_(n_.REAL,r.shape,a.shape),c=new r_(n_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Ra({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Tz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new qu(a_,r.shape,a.shape):i=new gl(a_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var bP={kernelName:ws,backendName:"webgl",kernelFunc:s_};function vP(e,t,n){let r=[ai(e.shape),...si(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[ai(t),...si(t)],i=new Vw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!Vu(a.shape,l)&&!(c.texture!==null&&Vu(c.shape,l))?vP(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var kP={kernelName:_o,backendName:"webgl",kernelFunc:Ae},i_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` + `}},u_="return a * b;";function c_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new l_(o_.REAL,r.shape,a.shape),c=new l_(o_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Fa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Mz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Xu(u_,r.shape,a.shape):i=new xl(u_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var SP={kernelName:_s,backendName:"webgl",kernelFunc:c_};function TP(e,t,n){let r=[si(e.shape),...ii(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[si(t),...ii(t)],i=new qw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!Uu(a.shape,l)&&!(c.texture!==null&&Uu(c.shape,l))?TP(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var EP={kernelName:bo,backendName:"webgl",kernelFunc:Ae},h_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` if (inIdx < 0 || inIdx >= ${a}) { return 0.0; } @@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},IP=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` + `}},CP=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function NP(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ui(e,t,n,r){let a=NP(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=it(this.rank),a=Bw("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=it(this.rank),a=Gw("rc",this.rank),s=new Array(this.rank);for(let u=0;u=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,m]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],x=r?[g,m,d]:[g,d,m],N=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),T=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],M=Math.max(y,g),D=n?N.shape[1]:N.shape[2],L=s!=null,W=i!=null,U=l==="leakyrelu",j=l!=null?Gd(l,!0):null,X=L||W||U||j!=null,G;if((p===1||m===1)&&D>o_&&X===!1){let Y=N,ae=T;n&&(Y=xn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=xn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=m!==1,ie=m===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,D,1]}}),E.push(Q));let ce=m===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,D]}}),E.push(oe));let pe=s_({inputs:{a:Q,b:oe},backend:a});G=vm({inputs:{x:pe},backend:a,attrs:{axis:ce,keepDims:!0}}),E.push(pe)}else{let Y=Yn(e.dtype,t.dtype),ae=new t_(w,x,[M,p,m],n,r,L,j,W,U),te=[N,T];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function MP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Xd({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var $P={kernelName:Ws,backendName:"webgl",kernelFunc:MP},l_="return abs(x);";function OP(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Ww(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Al(r.shape,l_):a=new Ca(r.shape,l_),n.runWebGLProgram(a,[r],r.dtype)}var DP={kernelName:Di,backendName:"webgl",kernelFunc:OP},zP=hr+` + `}};function Xd(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $P(e.shape,t):new MP(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function OP(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=Xd(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let m=v.sizeFromShape(d),f=v.sizeFromShape(e.shape)/m,A=Ae({inputs:{x:c},attrs:{shape:[f,m]},backend:r}),y=Lh(e.dtype),g=ci(A,y,"sum",r),_=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),_}function km(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return OP(a,s,i,n)}var DP={kernelName:Os,backendName:"webgl",kernelFunc:km};function xn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,m]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],x=r?[g,m,d]:[g,d,m],N=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),T=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],M=Math.max(y,g),D=n?N.shape[1]:N.shape[2],L=s!=null,W=i!=null,U=l==="leakyrelu",H=l!=null?qd(l,!0):null,X=L||W||U||H!=null,G;if((p===1||m===1)&&D>d_&&X===!1){let Y=N,ae=T;n&&(Y=xn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=xn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=m!==1,ie=m===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,D,1]}}),E.push(Q));let ce=m===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,D]}}),E.push(oe));let pe=c_({inputs:{a:Q,b:oe},backend:a});G=km({inputs:{x:pe},backend:a,attrs:{axis:ce,keepDims:!0}}),E.push(pe)}else{let Y=Jn(e.dtype,t.dtype),ae=new i_(w,x,[M,p,m],n,r,L,H,W,U),te=[N,T];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function PP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Kd({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var LP={kernelName:Bs,backendName:"webgl",kernelFunc:PP},p_="return abs(x);";function WP(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=jw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,p_):a=new Ra(r.shape,p_),n.runWebGLProgram(a,[r],r.dtype)}var BP={kernelName:zi,backendName:"webgl",kernelFunc:WP},VP=fr+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,PP=He({opSnippet:zP}),LP={kernelName:zi,backendName:"webgl",kernelFunc:PP},WP=hr+` +`,UP=Ge({opSnippet:VP}),HP={kernelName:Pi,backendName:"webgl",kernelFunc:UP},jP=fr+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,backendName:"webgl",kernelFunc:BP},u_="return a + b;",UP=Qt({opSnippet:u_,packedOpSnippet:u_,supportsComplex:!0,cpuKernelImpl:pz}),jP={kernelName:ma,backendName:"webgl",kernelFunc:UP},HP=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,GP=Ge({opSnippet:jP}),qP={kernelName:Li,backendName:"webgl",kernelFunc:GP},f_="return a + b;",XP=Qt({opSnippet:f_,packedOpSnippet:f_,supportsComplex:!0,cpuKernelImpl:gz}),KP={kernelName:Aa,backendName:"webgl",kernelFunc:XP},ZP=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back float result = ${r}; setOutput(result); } - `}},GP=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` + `}},YP=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back vec4 result = ${r}; setOutput(result); } - `}};function Kd(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Fn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Kd({inputs:r.slice(0,o),backend:n}),u=Kd({inputs:r.slice(o),backend:n});return Kd({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Yn(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new GP(r[0].shape,s):new HP(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var qP={kernelName:Xa,backendName:"webgl",kernelFunc:Kd};function XP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ui(f,f.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var KP={kernelName:rh,backendName:"webgl",kernelFunc:XP};function ZP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ui(f,f.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var YP={kernelName:ah,backendName:"webgl",kernelFunc:ZP},JP=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Zd(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Fn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Zd({inputs:r.slice(0,o),backend:n}),u=Zd({inputs:r.slice(o),backend:n});return Zd({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Jn(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new YP(r[0].shape,s):new ZP(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var JP={kernelName:Ka,backendName:"webgl",kernelFunc:Zd};function QP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ci(f,f.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var eL={kernelName:ah,backendName:"webgl",kernelFunc:QP};function tL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ci(f,f.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var nL={kernelName:sh,backendName:"webgl",kernelFunc:tL},rL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1258,7 +1258,7 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back } setOutput(float(bestIndex)); } - `}},QP=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=it(o),u=ln("coords",o),c,h;if(s===1){h=o+1;let N=it(h);c=` + `}},aL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=it(o),u=ln("coords",o),c,h;if(s===1){h=o+1;let N=it(h);c=` ${N} sourceLocR = ${N}(${u.join()}, 0); ++${u[o-1]}; ${N} sourceLocG = ${N}(${u.join()}, 0); @@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back } setOutput(bestIndex); } - `}};function c_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new JP(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=c_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function h_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new QP(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=h_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function d_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=c_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return h_(e,t,r)}function eL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=d_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var tL={kernelName:Ka,backendName:"webgl",kernelFunc:eL};function nL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=d_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var rL={kernelName:ql,backendName:"webgl",kernelFunc:nL},aL=hr+` + `}};function m_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new rL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=m_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function A_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new aL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=A_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function y_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=m_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return A_(e,t,r)}function sL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=y_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var iL={kernelName:Za,backendName:"webgl",kernelFunc:sL};function oL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=y_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var lL={kernelName:Xl,backendName:"webgl",kernelFunc:oL},uL=fr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,sL=He({opSnippet:aL}),iL={kernelName:Li,backendName:"webgl",kernelFunc:sL},oL=hr+"return log(x + sqrt(x * x + 1.0));",lL=He({opSnippet:oL}),uL={kernelName:Wi,backendName:"webgl",kernelFunc:lL},cL=hr+` +`,cL=Ge({opSnippet:uL}),hL={kernelName:Wi,backendName:"webgl",kernelFunc:cL},dL=fr+"return log(x + sqrt(x * x + 1.0));",pL=Ge({opSnippet:dL}),fL={kernelName:Bi,backendName:"webgl",kernelFunc:pL},mL=fr+` return atan(x); -`,hL=He({opSnippet:cL}),dL={kernelName:Bi,backendName:"webgl",kernelFunc:hL},pL=wP+` +`,AL=Ge({opSnippet:mL}),yL={kernelName:Vi,backendName:"webgl",kernelFunc:AL},gL=IP+` return atan(a, b); -`,fL=` +`,xL=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); - `+_P+` + `+NP+` return result; -`,mL=Qt({opSnippet:pL,packedOpSnippet:fL}),AL={kernelName:Ui,backendName:"webgl",kernelFunc:mL},yL=hr+` +`,wL=Qt({opSnippet:gL,packedOpSnippet:xL}),_L={kernelName:Hi,backendName:"webgl",kernelFunc:wL},bL=fr+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelName:Vi,backendName:"webgl",kernelFunc:gL},Xu=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vL=Ge({opSnippet:bL}),kL={kernelName:Ui,backendName:"webgl",kernelFunc:vL},Ku=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); @@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(${_}); } - `}},km=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` + `}},Im=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${y}); @@ -1640,7 +1640,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(${w}); } } - `}};function wL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Xu(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var _L={kernelName:Za,backendName:"webgl",kernelFunc:wL};function bL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new km(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var vL={kernelName:Xl,backendName:"webgl",kernelFunc:bL},kL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` + `}};function IL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Ku(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var NL={kernelName:Ya,backendName:"webgl",kernelFunc:IL};function SL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new Im(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var TL={kernelName:Kl,backendName:"webgl",kernelFunc:SL},EL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${h}); @@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},IL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` + `}},CL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); const float avgMultiplier = float(${A}); @@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function NL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new IL(d);return n.runWebGLProgram(p,[a],i.dtype)}var SL={kernelName:ih,backendName:"webgl",kernelFunc:NL};function TL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;cl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new kL(c);return n.runWebGLProgram(h,[a],i.dtype)}var EL={kernelName:sh,backendName:"webgl",kernelFunc:TL};function CL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Xd({a,b:s,transposeA:i,transposeB:o,backend:n})}var RL={kernelName:Ya,backendName:"webgl",kernelFunc:CL},FL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function RL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new CL(d);return n.runWebGLProgram(p,[a],i.dtype)}var FL={kernelName:oh,backendName:"webgl",kernelFunc:RL};function ML(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;hl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new EL(c);return n.runWebGLProgram(h,[a],i.dtype)}var $L={kernelName:ih,backendName:"webgl",kernelFunc:ML};function OL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Kd({a,b:s,transposeA:i,transposeB:o,backend:n})}var DL={kernelName:Ja,backendName:"webgl",kernelFunc:OL},zL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},ML=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},PL=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput((x - mean) * inv + offset); } - `}},$L=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new ML(r.shape,a.shape,s.shape,c,h,l):new FL(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},OL={kernelName:us,backendName:"webgl",kernelFunc:$L},zL=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=`uniform int start[${this.rank}];`,r=DL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Im[o]} = start[${o}] + coords.${Im[o]};`);a=` + `}},LL=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new PL(r.shape,a.shape,s.shape,c,h,l):new zL(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},WL={kernelName:cs,backendName:"webgl",kernelFunc:LL},VL=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=`uniform int start[${this.rank}];`,r=BL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Nm[o]} = start[${o}] + coords.${Nm[o]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` @@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${a} setOutput(getSource(${r})); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Im=["x","y","z","w","u","v"];function DL(e){if(e===1)return"sourceLoc";if(e<=6)return Im.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var PL=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=ln("coords",this.rank),r=ln("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Nm=["x","y","z","w","u","v"];function BL(e){if(e===1)return"sourceLoc";if(e<=6)return Nm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var UL=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=ln("coords",this.rank),r=ln("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; @@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${o} setOutput(result); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function LL(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=an.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ku(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=an.parseSliceParams(a,s,i);if(an.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Mz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=an.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PL(l):new zL(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),LL(a,o,l,n)}var WL={kernelName:Io,backendName:"webgl",kernelFunc:Ku},BL=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],m=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:c}}),y=Ku({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},VL={kernelName:Kl,backendName:"webgl",kernelFunc:BL};function UL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Lw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var jL={kernelName:oh,backendName:"webgl",kernelFunc:UL},HL="return float(a != b);",p_=Qt({opSnippet:HL,dtype:"bool"}),GL={kernelName:po,backendName:"webgl",kernelFunc:p_};function Zu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.real},backend:n})}var qL={kernelName:Th,backendName:"webgl",kernelFunc:Zu},XL="return float(int(x));";function KL(e,t){let n=new Ca(e.shape,XL),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Nm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Fn({inputs:{x:a},backend:n});let i=Nt(a.shape),o=Nm({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ra({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Zu({inputs:{input:a},backend:n}),o=Nm({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Fn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return KL(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=p_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var ZL={kernelName:Ja,backendName:"webgl",kernelFunc:Nm},f_="return ceil(x);",YL=He({opSnippet:f_,packedOpSnippet:f_,cpuKernelImpl:mz}),JL={kernelName:Qa,backendName:"webgl",kernelFunc:YL},QL=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function HL(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=an.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Zu(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=an.parseSliceParams(a,s,i);if(an.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Pz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=an.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UL(l):new VL(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),HL(a,o,l,n)}var jL={kernelName:No,backendName:"webgl",kernelFunc:Zu},GL=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],m=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:c}}),y=Zu({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},qL={kernelName:Zl,backendName:"webgl",kernelFunc:GL};function XL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Hw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var KL={kernelName:lh,backendName:"webgl",kernelFunc:XL},ZL="return float(a != b);",g_=Qt({opSnippet:ZL,dtype:"bool"}),YL={kernelName:fo,backendName:"webgl",kernelFunc:g_};function Yu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.real},backend:n})}var JL={kernelName:Eh,backendName:"webgl",kernelFunc:Yu},QL="return float(int(x));";function eW(e,t){let n=new Ra(e.shape,QL),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Sm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Fn({inputs:{x:a},backend:n});let i=St(a.shape),o=Sm({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Fa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Yu({inputs:{input:a},backend:n}),o=Sm({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Fn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return eW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=g_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var tW={kernelName:Qa,backendName:"webgl",kernelFunc:Sm},x_="return ceil(x);",nW=Ge({opSnippet:x_,packedOpSnippet:x_,cpuKernelImpl:wz}),rW={kernelName:es,backendName:"webgl",kernelFunc:nW},aW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},eW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},sW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function tW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new eW(a.shape):o=new QL(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var nW={kernelName:Aa,backendName:"webgl",kernelFunc:tW},rW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function iW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new sW(a.shape):o=new aW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var oW={kernelName:ya,backendName:"webgl",kernelFunc:iW},lW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function m_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function aW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new rW(r.shape),i=[m_(r,a.complexTensorInfos.real),m_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var sW={kernelName:Zl,backendName:"webgl",kernelFunc:aW},iW=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( - getT${m}(${Zd(i,l,f)}), - vec2(${Zd(u,l,f)})); + getT${m}(${Yd(i,l,f)}), + vec2(${Yd(u,l,f)})); }`}let d=o.length,p=o[o.length-1];h+=` return getChannel( - getT${d}(${Zd(i,l,p)}), - vec2(${Zd(u,l,p)}));`,this.userCode=` + getT${d}(${Yd(i,l,p)}), + vec2(${Yd(u,l,p)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${h} } @@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(result); } - `}};function Zd(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Yd(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var lW={kernelName:_h,backendName:"webgl",kernelFunc:Yd};function xl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>Zu({inputs:{input:m},backend:n})),c=e.map(m=>Yd({inputs:{input:m},backend:n})),h=xl(u,t,n),d=xl(c,t,n),p=Ra({inputs:{real:h,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=A_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=Az(h,c,r,d),m=C.computeOutShape(e.map(A=>A.shape),t),f=n.makeTensorInfo(m,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),f}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=xl(e.slice(0,u),t,n),h=xl(e.slice(u),t,n),d=xl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new oW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=A_(e,t,n),i=new iW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function A_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function y_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Fn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),xl(o,s,n)}var uW={kernelName:ji,backendName:"webgl",kernelFunc:y_},g_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,_="",b="";n&&(r?_=`float activation(float a) { + `}};function Yd(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Jd(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var pW={kernelName:bh,backendName:"webgl",kernelFunc:Jd};function wl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>Yu({inputs:{input:m},backend:n})),c=e.map(m=>Jd({inputs:{input:m},backend:n})),h=wl(u,t,n),d=wl(c,t,n),p=Fa({inputs:{real:h,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=__(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=_z(h,c,r,d),m=C.computeOutShape(e.map(A=>A.shape),t),f=n.makeTensorInfo(m,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),f}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=wl(e.slice(0,u),t,n),h=wl(e.slice(u),t,n),d=wl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new dW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=__(e,t,n),i=new hW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function __(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function b_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Fn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),wl(o,s,n)}var fW={kernelName:ji,backendName:"webgl",kernelFunc:b_},v_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,_="",b="";n&&(r?_=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?_=`float activation(float a) { @@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${b} setOutput(result); } - `}},cW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},mW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${a}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${r}); @@ -2110,7 +2110,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},hW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=on(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=` + `}},AW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=on(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=` blockIndex = rc.y + ${w}; pos = rc.x + ${b}; @@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${f.output} = result; } - `}};function x_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&c>o_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Xd({a:w,b:x,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Vu(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Xd({a:w,b:N,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Fn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function w_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*u*c,A=d*h,y=[f,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(x);let N=new hW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=Ae({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",W=o?Gd(o,!0):null,U=new t_(E.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,L),j=[E,x];if(a&&j.push(a),D&&j.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function dW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=x_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=w_({x:a,filter:s,convInfo:d,backend:n});else{let f=new g_(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var pW={kernelName:es,backendName:"webgl",kernelFunc:dW},fW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function k_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&c>d_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Kd({a:w,b:x,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Uu(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Kd({a:w,b:N,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Fn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function I_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*u*c,A=d*h,y=[f,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(x);let N=new AW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=Ae({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",W=o?qd(o,!0):null,U=new i_(E.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,L),H=[E,x];if(a&&H.push(a),D&&H.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function yW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=k_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=I_({x:a,filter:s,convInfo:d,backend:n});else{let f=new v_(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var gW={kernelName:ts,backendName:"webgl",kernelFunc:yW},xW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},mW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` + `}},wW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},AW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},_W=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},yW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` + `}},bW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function gW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new fW(d);return n.runWebGLProgram(p,[a,s],"float32")}var xW={kernelName:uh,backendName:"webgl",kernelFunc:gW};function wW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new mW(d);return n.runWebGLProgram(p,[a,s],"float32")}var _W={kernelName:ts,backendName:"webgl",kernelFunc:wW};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new cW(u);return n.runWebGLProgram(c,[a,s],"float32")}var vW={kernelName:Yl,backendName:"webgl",kernelFunc:bW};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new AW(u);return n.runWebGLProgram(c,[a,s],"float32")}var IW={kernelName:ch,backendName:"webgl",kernelFunc:kW};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new yW(u);return n.runWebGLProgram(c,[a,s],"float32")}var SW={kernelName:hh,backendName:"webgl",kernelFunc:NW},TW=e_+` + `}};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new xW(d);return n.runWebGLProgram(p,[a,s],"float32")}var kW={kernelName:ch,backendName:"webgl",kernelFunc:vW};function IW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new wW(d);return n.runWebGLProgram(p,[a,s],"float32")}var NW={kernelName:ns,backendName:"webgl",kernelFunc:IW};function SW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new mW(u);return n.runWebGLProgram(c,[a,s],"float32")}var TW={kernelName:Jl,backendName:"webgl",kernelFunc:SW};function EW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new _W(u);return n.runWebGLProgram(c,[a,s],"float32")}var CW={kernelName:hh,backendName:"webgl",kernelFunc:EW};function RW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new bW(u);return n.runWebGLProgram(c,[a,s],"float32")}var FW={kernelName:dh,backendName:"webgl",kernelFunc:RW},MW=s_+` return cos(x); -`,EW=He({opSnippet:TW}),CW={kernelName:ns,backendName:"webgl",kernelFunc:EW},RW=` +`,$W=Ge({opSnippet:MW}),OW={kernelName:rs,backendName:"webgl",kernelFunc:$W},DW=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,FW=He({opSnippet:RW}),MW={kernelName:Hi,backendName:"webgl",kernelFunc:FW},$W=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,zW=Ge({opSnippet:DW}),PW={kernelName:Gi,backendName:"webgl",kernelFunc:zW},LW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${g}); void main() { @@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(newValue); } } - `}},OW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new $W(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},DW={kernelName:Gi,backendName:"webgl",kernelFunc:OW},v_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${__(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` + `}},WW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new LW(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},BW={kernelName:qi,backendName:"webgl",kernelFunc:WW},T_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${N_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${it(r)} coords = getOutputCoords(); - int end = ${b_(r,"coords")}; + int end = ${S_(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; - ${b_(r,"coords")} = idx; - val += getX(${__(r,"coords")}); + ${S_(r,"coords")} = idx; + val += getX(${N_(r,"coords")}); } setOutput(val); } - `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function __(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function b_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function zW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=xn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Fn({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new v_(c.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new v_(c.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=C.getUndoAxesPermutation(u),f=xn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),f}return p}var PW={kernelName:rs,backendName:"webgl",kernelFunc:zW};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Lw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=fz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var WW={kernelName:dh,backendName:"webgl",kernelFunc:LW},BW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function N_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function S_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function VW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=xn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Fn({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new T_(c.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new T_(c.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=C.getUndoAxesPermutation(u),f=xn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),f}return p}var UW={kernelName:as,backendName:"webgl",kernelFunc:VW};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Hw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=xz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var jW={kernelName:ph,backendName:"webgl",kernelFunc:HW},GW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function VW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new BW(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var UW={kernelName:qi,backendName:"webgl",kernelFunc:VW},k_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function qW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new GW(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var XW={kernelName:Xi,backendName:"webgl",kernelFunc:qW},E_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { @@ -2503,7 +2503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${y} setOutput(result); } - `}},I_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=m,A="int xR; int xC; int xCOffset;";for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new I_(h):d=new k_(h),n.runWebGLProgram(d,[a,s],"float32")}var HW={kernelName:as,backendName:"webgl",kernelFunc:jW},GW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function KW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new C_(h):d=new E_(h),n.runWebGLProgram(d,[a,s],"float32")}var ZW={kernelName:ss,backendName:"webgl",kernelFunc:KW},YW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},qW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},JW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -2736,13 +2736,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function XW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new GW(h);return n.runWebGLProgram(d,[a,s],"float32")}var KW={kernelName:ph,backendName:"webgl",kernelFunc:XW};function ZW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new qW(h);return n.runWebGLProgram(d,[a,s],"float32")}var YW={kernelName:fh,backendName:"webgl",kernelFunc:ZW},JW=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function QW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new YW(h);return n.runWebGLProgram(d,[a,s],"float32")}var eB={kernelName:fh,backendName:"webgl",kernelFunc:QW};function tB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new JW(h);return n.runWebGLProgram(d,[a,s],"float32")}var nB={kernelName:mh,backendName:"webgl",kernelFunc:tB},rB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function QW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new JW(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var eB={kernelName:mh,backendName:"webgl",kernelFunc:QW},tB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` + `}};function aB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new rB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var sB={kernelName:Ah,backendName:"webgl",kernelFunc:aB},iB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${c}, ${h}); const float neg_infinity = -3.4e38; @@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float result = curVal; setOutput(result); } - `}};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new tB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var rB={kernelName:Jl,backendName:"webgl",kernelFunc:nB},aB="return (x >= 0.0) ? x : (exp(x) - 1.0);",sB=` + `}};function oB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new iB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var lB={kernelName:Ql,backendName:"webgl",kernelFunc:oB},uB="return (x >= 0.0) ? x : (exp(x) - 1.0);",cB=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,iB=He({opSnippet:aB,packedOpSnippet:sB}),oB={kernelName:Xi,backendName:"webgl",kernelFunc:iB},lB="return (b >= 1.0) ? a : a * (b + 1.0);",uB=` +`,hB=Ge({opSnippet:uB,packedOpSnippet:cB}),dB={kernelName:Ki,backendName:"webgl",kernelFunc:hB},pB="return (b >= 1.0) ? a : a * (b + 1.0);",fB=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,cB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(uB,r.shape,a.shape):new gl(lB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},hB={kernelName:gh,backendName:"webgl",kernelFunc:cB},dB=` +`,mB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(fB,r.shape,a.shape):new xl(pB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},AB={kernelName:xh,backendName:"webgl",kernelFunc:mB},yB=` return vec4(equal(a, b)); -`,pB="return float(a == b);",fB=Qt({opSnippet:pB,packedOpSnippet:dB,dtype:"bool"}),mB={kernelName:Zi,backendName:"webgl",kernelFunc:fB},AB=` +`,gB="return float(a == b);",xB=Qt({opSnippet:gB,packedOpSnippet:yB,dtype:"bool"}),wB={kernelName:Yi,backendName:"webgl",kernelFunc:xB},_B=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,yB=He({opSnippet:AB}),gB={kernelName:Ki,backendName:"webgl",kernelFunc:yB},N_="return exp(x);",S_=He({opSnippet:N_,packedOpSnippet:N_,cpuKernelImpl:yz}),xB={kernelName:is,backendName:"webgl",kernelFunc:S_};function Sm(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var wB={kernelName:Yi,backendName:"webgl",kernelFunc:Sm},T_="return exp(x) - 1.0;",_B=He({opSnippet:T_,packedOpSnippet:T_,cpuKernelImpl:gz}),bB={kernelName:Ji,backendName:"webgl",kernelFunc:_B},E_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,bB=Ge({opSnippet:_B}),vB={kernelName:Zi,backendName:"webgl",kernelFunc:bB},R_="return exp(x);",F_=Ge({opSnippet:R_,packedOpSnippet:R_,cpuKernelImpl:bz}),kB={kernelName:os,backendName:"webgl",kernelFunc:F_};function Tm(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var IB={kernelName:Ji,backendName:"webgl",kernelFunc:Tm},M_="return exp(x) - 1.0;",NB=Ge({opSnippet:M_,packedOpSnippet:M_,cpuKernelImpl:vz}),SB={kernelName:Qi,backendName:"webgl",kernelFunc:NB},$_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${a}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function C_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new E_("real",l,t),c=new E_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Ra({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function vB(e){let{inputs:t,backend:n}=e,{input:r}=t;return C_(r,!1,n)}var kB={kernelName:xh,backendName:"webgl",kernelFunc:vB},IB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function O_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new $_("real",l,t),c=new $_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Fa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function TB(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!1,n)}var EB={kernelName:wh,backendName:"webgl",kernelFunc:TB},CB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } - `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Tm(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new IB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var NB={kernelName:Ql,backendName:"webgl",kernelFunc:Tm},SB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Em(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new CB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var RB={kernelName:eu,backendName:"webgl",kernelFunc:Em},FB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(outputValue); } - `}},TB={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new SB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},R_="return floor(x);",EB=He({opSnippet:R_,packedOpSnippet:R_,cpuKernelImpl:xz}),CB={kernelName:os,backendName:"webgl",kernelFunc:EB},RB=` + `}},MB={kernelName:eo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new FB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},D_="return floor(x);",$B=Ge({opSnippet:D_,packedOpSnippet:D_,cpuKernelImpl:kz}),OB={kernelName:ls,backendName:"webgl",kernelFunc:$B},DB=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } else { return NAN; } -`,FB=` +`,zB=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,MB=Qt({opSnippet:RB,packedOpSnippet:FB,dtype:"int32"}),$B={kernelName:ls,backendName:"webgl",kernelFunc:MB},OB=class{constructor(e){this.variableNames=["A"];let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` +`,PB=Qt({opSnippet:DB,packedOpSnippet:zB,dtype:"int32"}),LB={kernelName:us,backendName:"webgl",kernelFunc:PB},WB=class{constructor(e){this.variableNames=["A"];let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},DB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` + `}},BB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${t.output} = result; } - `}},PB={kernelName:Mh,backendName:"webgl",kernelFunc:zB},wl;function zB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(wl==null&&(wl=document.createElement("canvas").getContext("2d")),wl.canvas.width=u,wl.canvas.height=c,wl.drawImage(a,0,0,u,c),a=wl.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Hn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let m=J().getBool("WEBGL_PACK")?new DB(d):new OB(d),f=n.runWebGLProgram(m,[p],"int32");return n.disposeData(p.dataId),f}function LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=x_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=w_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?Gd(p,!1):null,T=new g_(A,b,N,w,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var WB={kernelName:Bs,backendName:"webgl",kernelFunc:LB};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=c;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?Gd(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(E),m.push(E)}let N;y?N=new I_(A,b,g,w,x):N=new k_(A,b,g,w,x);let T=n.runWebGLProgram(N,_,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var VB={kernelName:Vs,backendName:"webgl",kernelFunc:BB},UB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=it(t.length),a=it(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` + `}},UB={kernelName:$h,backendName:"webgl",kernelFunc:VB},_l;function VB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(_l==null&&(_l=document.createElement("canvas").getContext("2d")),_l.canvas.width=u,_l.canvas.height=c,_l.drawImage(a,0,0,u,c),a=_l.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Gn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let m=J().getBool("WEBGL_PACK")?new BB(d):new WB(d),f=n.runWebGLProgram(m,[p],"int32");return n.disposeData(p.dataId),f}function HB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=k_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=I_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?qd(p,!1):null,T=new v_(A,b,N,w,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var jB={kernelName:Vs,backendName:"webgl",kernelFunc:HB};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=c;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?qd(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(E),m.push(E)}let N;y?N=new C_(A,b,g,w,x):N=new E_(A,b,g,w,x);let T=n.runWebGLProgram(N,_,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var qB={kernelName:Us,backendName:"webgl",kernelFunc:GB},XB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=it(t.length),a=it(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${r} strides = ${r}(${this.strides}); void main() { ${a} coords = getOutputCoords(); @@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(getX(flattenIndex, coords[1])); } - `}};function jB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new UB(i,c,[l,u]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var HB={kernelName:to,backendName:"webgl",kernelFunc:jB},qB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=it(this.rank),r=GB(e,2);this.userCode=` + `}};function KB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new XB(i,c,[l,u]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var ZB={kernelName:no,backendName:"webgl",kernelFunc:KB},JB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=it(this.rank),r=YB(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${r})); } - `}};function GB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new qB(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var KB={kernelName:eo,backendName:"webgl",kernelFunc:XB},ZB="return float(a > b);",YB=` + `}};function YB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new JB(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var eV={kernelName:to,backendName:"webgl",kernelFunc:QB},tV="return float(a > b);",nV=` return vec4(greaterThan(a, b)); -`,JB=Qt({opSnippet:ZB,packedOpSnippet:YB,cpuKernelImpl:_z,dtype:"bool"}),QB={kernelName:no,backendName:"webgl",kernelFunc:JB},eV="return float(a >= b);",tV=` +`,rV=Qt({opSnippet:tV,packedOpSnippet:nV,cpuKernelImpl:Nz,dtype:"bool"}),aV={kernelName:ro,backendName:"webgl",kernelFunc:rV},sV="return float(a >= b);",iV=` return vec4(greaterThanEqual(a, b)); -`,nV=Qt({opSnippet:eV,packedOpSnippet:tV,dtype:"bool"}),rV={kernelName:cs,backendName:"webgl",kernelFunc:nV};function aV(e){let{inputs:t,backend:n}=e,{input:r}=t;return C_(r,!0,n)}var sV={kernelName:wh,backendName:"webgl",kernelFunc:aV},iV="return float(!isnan(x) && !isinf(x));",oV=He({opSnippet:iV,dtype:"bool"}),lV={kernelName:ro,backendName:"webgl",kernelFunc:oV},uV="return float(isinf(x));",cV=He({opSnippet:uV,dtype:"bool"}),hV={kernelName:ao,backendName:"webgl",kernelFunc:cV},dV="return float(isnan(x));",pV=He({opSnippet:dV,dtype:"bool"}),fV={kernelName:so,backendName:"webgl",kernelFunc:pV},mV="return float(a < b);",AV=` +`,oV=Qt({opSnippet:sV,packedOpSnippet:iV,dtype:"bool"}),lV={kernelName:hs,backendName:"webgl",kernelFunc:oV};function uV(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!0,n)}var cV={kernelName:_h,backendName:"webgl",kernelFunc:uV},hV="return float(!isnan(x) && !isinf(x));",dV=Ge({opSnippet:hV,dtype:"bool"}),pV={kernelName:ao,backendName:"webgl",kernelFunc:dV},fV="return float(isinf(x));",mV=Ge({opSnippet:fV,dtype:"bool"}),AV={kernelName:so,backendName:"webgl",kernelFunc:mV},yV="return float(isnan(x));",gV=Ge({opSnippet:yV,dtype:"bool"}),xV={kernelName:io,backendName:"webgl",kernelFunc:gV},wV="return float(a < b);",_V=` return vec4(lessThan(a, b)); -`,yV=Qt({opSnippet:mV,packedOpSnippet:AV,cpuKernelImpl:bz,dtype:"bool"}),gV={kernelName:io,backendName:"webgl",kernelFunc:yV},xV="return float(a <= b);",wV=` +`,bV=Qt({opSnippet:wV,packedOpSnippet:_V,cpuKernelImpl:Sz,dtype:"bool"}),vV={kernelName:oo,backendName:"webgl",kernelFunc:bV},kV="return float(a <= b);",IV=` return vec4(lessThanEqual(a, b)); -`,_V=Qt({opSnippet:xV,packedOpSnippet:wV,dtype:"bool"}),bV={kernelName:oo,backendName:"webgl",kernelFunc:_V};function vV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=vz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var kV={kernelName:bh,backendName:"webgl",kernelFunc:vV},IV=`if (x < 0.0) return NAN; - return log(x);`,NV=` +`,NV=Qt({opSnippet:kV,packedOpSnippet:IV,dtype:"bool"}),SV={kernelName:lo,backendName:"webgl",kernelFunc:NV};function TV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Tz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var EV={kernelName:vh,backendName:"webgl",kernelFunc:TV},CV=`if (x < 0.0) return NAN; + return log(x);`,RV=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; @@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result.a = isNaN.a == 1.0 ? NAN : result.a; return result; -`,SV=He({opSnippet:IV,packedOpSnippet:NV,cpuKernelImpl:kz}),TV={kernelName:ps,backendName:"webgl",kernelFunc:SV},EV="return log(1.0 + x);",CV=He({opSnippet:EV}),RV={kernelName:lo,backendName:"webgl",kernelFunc:CV},FV="return float(a >= 1.0 && b >= 1.0);",MV=` +`,FV=Ge({opSnippet:CV,packedOpSnippet:RV,cpuKernelImpl:Ez}),MV={kernelName:fs,backendName:"webgl",kernelFunc:FV},$V="return log(1.0 + x);",OV=Ge({opSnippet:$V}),DV={kernelName:uo,backendName:"webgl",kernelFunc:OV},zV="return float(a >= 1.0 && b >= 1.0);",PV=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,$V=Qt({opSnippet:FV,packedOpSnippet:MV,dtype:"bool"}),OV={kernelName:uo,backendName:"webgl",kernelFunc:$V},DV="return float(!(x >= 1.0));",zV=He({opSnippet:DV}),PV={kernelName:eu,backendName:"webgl",kernelFunc:zV},LV="return float(a >= 1.0 || b >= 1.0);",WV=` +`,LV=Qt({opSnippet:zV,packedOpSnippet:PV,dtype:"bool"}),WV={kernelName:co,backendName:"webgl",kernelFunc:LV},BV="return float(!(x >= 1.0));",VV=Ge({opSnippet:BV}),UV={kernelName:tu,backendName:"webgl",kernelFunc:VV},HV="return float(a >= 1.0 || b >= 1.0);",jV=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,BV=Qt({opSnippet:LV,packedOpSnippet:WV,dtype:"bool"}),VV={kernelName:tu,backendName:"webgl",kernelFunc:BV},UV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` +`,GV=Qt({opSnippet:HV,packedOpSnippet:jV,dtype:"bool"}),qV={kernelName:nu,backendName:"webgl",kernelFunc:GV},XV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float val = x * ${o}; setOutput(val); } - `}},jV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` + `}},KV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},HV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new jV(a.shape,s,i,o,l):new UV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},GV={kernelName:nu,backendName:"webgl",kernelFunc:HV},qV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` + `}},ZV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new KV(a.shape,s,i,o,l):new XV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},YV={kernelName:ru,backendName:"webgl",kernelFunc:ZV},JV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(result); } - `}},XV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new qV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},KV={kernelName:vh,backendName:"webgl",kernelFunc:XV};function ZV(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ui(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function F_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=_m(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=qd(a,c,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=Iz(g,v.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=ZV(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var YV={kernelName:fs,backendName:"webgl",kernelFunc:F_},JV=Kw+` + `}},QV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new JV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},eU={kernelName:kh,backendName:"webgl",kernelFunc:QV};function tU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function z_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=bm(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=Xd(a,c,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=Cz(g,v.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=tU(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var nU={kernelName:ms,backendName:"webgl",kernelFunc:z_},rU=e_+` return max(a, b); -`,QV=` +`,aU=` vec4 result = vec4(max(a, b)); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); - `+Hd+` + `+Gd+` return result; -`,eU=Qt({opSnippet:JV,packedOpSnippet:QV,cpuKernelImpl:Nz}),tU={kernelName:ms,backendName:"webgl",kernelFunc:eU};function nU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Xu(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var rU={kernelName:As,backendName:"webgl",kernelFunc:nU};function aU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new km(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var sU={kernelName:ru,backendName:"webgl",kernelFunc:aU},iU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` +`,sU=Qt({opSnippet:rU,packedOpSnippet:aU,cpuKernelImpl:Rz}),iU={kernelName:As,backendName:"webgl",kernelFunc:sU};function oU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Ku(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var lU={kernelName:ys,backendName:"webgl",kernelFunc:oU};function uU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new Im(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var cU={kernelName:au,backendName:"webgl",kernelFunc:uU},hU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},oU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` + `}},dU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${h}, ${d}); void main() { @@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function lU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new km(d,"max",!0),m=n.runWebGLProgram(p,[i],i.dtype),f=new oU(d),A=n.runWebGLProgram(f,[a,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var uU={kernelName:Ih,backendName:"webgl",kernelFunc:lU};function cU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;cl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,m=new Xu(d,"max",p),f=n.runWebGLProgram(m,[o],o.dtype),A=new iU(d),y=n.runWebGLProgram(A,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var hU={kernelName:kh,backendName:"webgl",kernelFunc:cU};function dU(e,t,n,r){let a=new Xu(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new Xu(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var pU={kernelName:Nh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,a,s,u,i),[h,d]=dU(r,o,c,l);return[h,d]}};function fU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ui(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var mU={kernelName:ys,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let _=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;N{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,a,s,u,i),[h,d]=yU(r,o,c,l);return[h,d]}};function xU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var wU={kernelName:gs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let _=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;Nu[0]+e[c]+u[1]);let r=e.length,a=it(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=` +`,IU=Qt({opSnippet:vU,packedOpSnippet:kU,cpuKernelImpl:Fz}),NU={kernelName:ws,backendName:"webgl",kernelFunc:IU},SU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=it(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${a} coords = outC - start; setOutput(getX(${o})); } - `}},vU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=it(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` + `}},TU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=it(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` ${a} source = rc; if (source < start) { source = start * 2 - source - ${h}; @@ -3337,13 +3337,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${d} setOutput(result); } - `}},kU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vU(r.shape,a,s):new bU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},IU={kernelName:au,backendName:"webgl",kernelFunc:kU},NU=`if (b == 0.0) return NAN; - return mod(a, b);`,SU=` + `}},EU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TU(r.shape,a,s):new SU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},CU={kernelName:su,backendName:"webgl",kernelFunc:EU},RU=`if (b == 0.0) return NAN; + return mod(a, b);`,FU=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); - `+Hd+` + `+Gd+` return result; -`,TU=Qt({opSnippet:NU,packedOpSnippet:SU}),EU={kernelName:co,backendName:"webgl",kernelFunc:TU},CU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` +`,MU=Qt({opSnippet:RU,packedOpSnippet:FU}),$U={kernelName:ho,backendName:"webgl",kernelFunc:MU},OU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; void main() { @@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},RU=` + `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},DU=` if (a == b) { return 1.0; }; -return a / b;`,FU=` +return a / b;`,zU=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3387,14 +3387,14 @@ return a / b;`,FU=` } return result; -`,M_=Qt({opSnippet:RU,packedOpSnippet:FU,checkOutOfBounds:!0}),MU={kernelName:ss,backendName:"webgl",kernelFunc:M_},$_="return a - b;",O_=Qt({opSnippet:$_,packedOpSnippet:$_,supportsComplex:!0,cpuKernelImpl:Oz}),$U={kernelName:zs,backendName:"webgl",kernelFunc:O_};function D_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=F_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=O_({inputs:{a,b:u},backend:n}),h=S_({inputs:{x:c},backend:n}),d=vm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),m=M_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var OU={kernelName:Os,backendName:"webgl",kernelFunc:D_};function DU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:D_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new CU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var zU={kernelName:Sh,backendName:"webgl",kernelFunc:DU},z_="return -x;";function PU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Ez(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Al(r.shape,z_):a=new Ca(r.shape,z_),n.runWebGLProgram(a,[r],r.dtype)}var LU={kernelName:ho,backendName:"webgl",kernelFunc:PU},WU=Rr.nonMaxSuppressionV3Impl;function BU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=WU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var VU={kernelName:fo,backendName:"webgl",kernelFunc:BU},UU=Rr.nonMaxSuppressionV4Impl;function jU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=UU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var HU={kernelName:mo,backendName:"webgl",kernelFunc:jU},GU=Rr.nonMaxSuppressionV5Impl;function qU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=GU(c,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var XU={kernelName:Ao,backendName:"webgl",kernelFunc:qU},KU=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`,P_=Qt({opSnippet:DU,packedOpSnippet:zU,checkOutOfBounds:!0}),PU={kernelName:is,backendName:"webgl",kernelFunc:P_},L_="return a - b;",W_=Qt({opSnippet:L_,packedOpSnippet:L_,supportsComplex:!0,cpuKernelImpl:Wz}),LU={kernelName:Ps,backendName:"webgl",kernelFunc:W_};function B_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=z_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=W_({inputs:{a,b:u},backend:n}),h=F_({inputs:{x:c},backend:n}),d=km({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),m=P_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var WU={kernelName:Ds,backendName:"webgl",kernelFunc:B_};function BU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:B_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new OU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var VU={kernelName:Th,backendName:"webgl",kernelFunc:BU},V_="return -x;";function UU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=$z(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,V_):a=new Ra(r.shape,V_),n.runWebGLProgram(a,[r],r.dtype)}var HU={kernelName:po,backendName:"webgl",kernelFunc:UU},jU=Mr.nonMaxSuppressionV3Impl;function GU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=jU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var qU={kernelName:mo,backendName:"webgl",kernelFunc:GU},XU=Mr.nonMaxSuppressionV4Impl;function KU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=XU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var ZU={kernelName:Ao,backendName:"webgl",kernelFunc:KU},YU=Mr.nonMaxSuppressionV5Impl;function JU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=YU(c,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var QU={kernelName:yo,backendName:"webgl",kernelFunc:JU},eH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } - `}},ZU=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new KU(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},YU={kernelName:_s,backendName:"webgl",kernelFunc:ZU};function Jd(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Zu({inputs:{input:r},backend:n}),s=Jd({inputs:{x:a},backend:n}),i=Yd({inputs:{input:r},backend:n}),o=Jd({inputs:{x:i},backend:n}),l=Ra({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Tm({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var JU={kernelName:$o,backendName:"webgl",kernelFunc:Jd};function P_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Zu({inputs:{input:r},backend:n}),s=P_({inputs:{x:a},backend:n}),i=Yd({inputs:{input:r},backend:n}),o=Jd({inputs:{x:i},backend:n}),l=Ra({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Tm({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var QU={kernelName:yo,backendName:"webgl",kernelFunc:P_};function ej(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Sm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Sm({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=y_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var tj={kernelName:go,backendName:"webgl",kernelFunc:ej},nj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=it(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` + `}},tH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new eH(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},nH={kernelName:bs,backendName:"webgl",kernelFunc:tH};function Qd(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Yu({inputs:{input:r},backend:n}),s=Qd({inputs:{x:a},backend:n}),i=Jd({inputs:{input:r},backend:n}),o=Qd({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Em({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var rH={kernelName:Oo,backendName:"webgl",kernelFunc:Qd};function U_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Yu({inputs:{input:r},backend:n}),s=U_({inputs:{x:a},backend:n}),i=Jd({inputs:{input:r},backend:n}),o=Qd({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Em({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var aH={kernelName:go,backendName:"webgl",kernelFunc:U_};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Tm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Tm({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=b_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var iH={kernelName:xo,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=it(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3419,7 +3419,7 @@ return a / b;`,FU=` setOutput(getX(${o})); } } - `}},rj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=it(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; + `}},lH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=it(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${u}) { `,r===1?"":`} rc = outputLoc; @@ -3443,7 +3443,7 @@ return a / b;`,FU=` ${p} setOutput(result); } - `}},L_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rj(a.shape,s,i):new nj(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},aj={kernelName:bs,backendName:"webgl",kernelFunc:L_},sj=` + `}},H_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lH(a.shape,s,i):new oH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},uH={kernelName:vs,backendName:"webgl",kernelFunc:H_},cH=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -3452,7 +3452,7 @@ return a / b;`,FU=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,ij=` +`,hH=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -3466,11 +3466,11 @@ return a / b;`,FU=` result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); - `+Hd+` + `+Gd+` return result; -`,oj=Qt({opSnippet:sj,packedOpSnippet:ij}),lj={kernelName:vs,backendName:"webgl",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=xn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=Cz(d.shape,d.dtype,m,c);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(f),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Ph(a.dtype),_=ui(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:m}}),l.push(y),l.push(_)}if(i){l.push(p);let m=C.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var cj={kernelName:xo,backendName:"webgl",kernelFunc:uj},W_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Rz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},hj={kernelName:su,backendName:"webgl",kernelFunc:W_},dj="return 1.0 / x;",pj=He({opSnippet:dj}),fj={kernelName:wo,backendName:"webgl",kernelFunc:pj},mj=hr+` +`,dH=Qt({opSnippet:cH,packedOpSnippet:hH}),pH={kernelName:ks,backendName:"webgl",kernelFunc:dH};function fH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=xn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=Oz(d.shape,d.dtype,m,c);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(f),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Lh(a.dtype),_=ci(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:m}}),l.push(y),l.push(_)}if(i){l.push(p);let m=C.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var mH={kernelName:wo,backendName:"webgl",kernelFunc:fH},j_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Dz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},AH={kernelName:iu,backendName:"webgl",kernelFunc:j_},yH="return 1.0 / x;",gH=Ge({opSnippet:yH}),xH={kernelName:_o,backendName:"webgl",kernelFunc:gH},wH=fr+` return (x < 0.0) ? 0.0 : x; -`,Aj=` +`,_H=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3480,9 +3480,9 @@ return a / b;`,FU=` result.a = isNaN.a ? x.a : result.a; return result; -`,yj=He({opSnippet:mj,packedOpSnippet:Aj}),gj={kernelName:Is,backendName:"webgl",kernelFunc:yj},xj=hr+` +`,bH=Ge({opSnippet:wH,packedOpSnippet:_H}),vH={kernelName:Ns,backendName:"webgl",kernelFunc:bH},kH=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,wj=` +`,IH=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3492,7 +3492,7 @@ return a / b;`,FU=` result.a = isNaN.a ? x.a : result.a; return result; -`,_j=He({opSnippet:xj,packedOpSnippet:wj}),bj={kernelName:Ss,backendName:"webgl",kernelFunc:_j},vj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,NH=Ge({opSnippet:kH,packedOpSnippet:IH}),SH={kernelName:Ts,backendName:"webgl",kernelFunc:NH},TH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3525,7 +3525,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}},kj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},EH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, @@ -3602,7 +3602,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}};function Ij(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kj(a.shape,l,u,s,i):new vj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var Nj={kernelName:Ns,backendName:"webgl",kernelFunc:Ij},Sj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new EH(a.shape,l,u,s,i):new TH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var RH={kernelName:Ss,backendName:"webgl",kernelFunc:CH},FH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3683,7 +3683,7 @@ return a / b;`,FU=` setOutput(accumulator); } - `}};function Tj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Sj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Ej={kernelName:Ch,backendName:"webgl",kernelFunc:Tj},Cj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function MH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new FH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var $H={kernelName:Rh,backendName:"webgl",kernelFunc:MH},OH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3705,7 +3705,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new Cj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var Fj={kernelName:iu,backendName:"webgl",kernelFunc:Rj},Mj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function DH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new OH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var zH={kernelName:ou,backendName:"webgl",kernelFunc:DH},PH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3775,7 +3775,7 @@ return a / b;`,FU=` setOutput(accumulator); } - `}};function $j(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Mj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Oj={kernelName:Eh,backendName:"webgl",kernelFunc:$j},Dj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function LH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new PH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var WH={kernelName:Ch,backendName:"webgl",kernelFunc:LH},BH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -3785,7 +3785,7 @@ return a / b;`,FU=` ${s} coords = getOutputCoords(); setOutput(getX(${a})); } - `}},zj=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=ln("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=it(n);n===1?this.userCode=` + `}},VH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=ln("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=it(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -3813,7 +3813,7 @@ return a / b;`,FU=` } setOutput(result); } - `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Fn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zj(a.shape,o):new Dj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Lj={kernelName:Ts,backendName:"webgl",kernelFunc:Pj},Wj=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` + `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function UH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Fn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VH(a.shape,o):new BH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var HH={kernelName:Es,backendName:"webgl",kernelFunc:UH},jH=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` vec3 fill = vec3(${n.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -3830,7 +3830,7 @@ return a / b;`,FU=` } setOutput(outputValue); } - `}},Bj={kernelName:Oo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Wj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},Vj=` + `}},GH={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new jH(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},qH=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -3845,7 +3845,7 @@ return a / b;`,FU=` return base + 1.0; } } -`,Uj=He({opSnippet:Vj}),jj={kernelName:Es,backendName:"webgl",kernelFunc:Uj},Hj="return inversesqrt(x);",Gj=He({opSnippet:Hj,cpuKernelImpl:Fz}),qj={kernelName:Cs,backendName:"webgl",kernelFunc:Gj},B_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=it(a.length),l=it(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` +`,XH=Ge({opSnippet:qH}),KH={kernelName:Cs,backendName:"webgl",kernelFunc:XH},ZH="return inversesqrt(x);",YH=Ge({opSnippet:ZH,cpuKernelImpl:zz}),JH={kernelName:Rs,backendName:"webgl",kernelFunc:YH},G_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=it(a.length),l=it(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${a}); void main() { @@ -3865,7 +3865,7 @@ return a / b;`,FU=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function Xj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=C.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return rb(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,_,M,b,w,x,N,T,E,L),D}var KG={kernelName:as,backendName:"wasm",setupFunc:qG,kernelFunc:XG},ZG=!1,YG=un(Zi,ZG,"bool"),JG=wn(is);function Cm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),dr({inputs:{x:a},backend:r,attrs:{shape:o}})}var QG={kernelName:Yi,backendName:"wasm",kernelFunc:Cm};function eq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var tq={kernelName:Ql,backendName:"wasm",kernelFunc:eq},ab;function nq(e){ab=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function rq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return ab(s,o,l,u,c,i),a}var aq={kernelName:Qi,backendName:"wasm",kernelFunc:rq,setupFunc:nq},sq=wn(os),iq=!1,oq=un(ls,iq),sb;function lq(e){sb=e.wasm.cwrap(us,null,["number","number","number","number","number","number","number"])}function uq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return sb(c,h,d,p,m,a,A),f}var cq={kernelName:us,backendName:"wasm",setupFunc:lq,kernelFunc:uq},ib;function hq(e){ib=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=Yu[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return ib(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,j,_,A,ie,m||0,te),ae}var pq={kernelName:Bs,backendName:"wasm",setupFunc:hq,kernelFunc:dq},ob;function fq(e){ob=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=Yu[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return lb(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,_,M,b,w,x,N,T,E,L),D}var eq={kernelName:ss,backendName:"wasm",setupFunc:JG,kernelFunc:QG},tq=!1,nq=un(Yi,tq,"bool"),rq=wn(os);function Rm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mr({inputs:{x:a},backend:r,attrs:{shape:o}})}var aq={kernelName:Ji,backendName:"wasm",kernelFunc:Rm};function sq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var iq={kernelName:eu,backendName:"wasm",kernelFunc:sq},ub;function oq(e){ub=e.wasm.cwrap(eo,null,["number","number","number","number","number","number"])}function lq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return ub(s,o,l,u,c,i),a}var uq={kernelName:eo,backendName:"wasm",kernelFunc:lq,setupFunc:oq},cq=wn(ls),hq=!1,dq=un(us,hq),cb;function pq(e){cb=e.wasm.cwrap(cs,null,["number","number","number","number","number","number","number"])}function fq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return cb(c,h,d,p,m,a,A),f}var mq={kernelName:cs,backendName:"wasm",setupFunc:pq,kernelFunc:fq},hb;function Aq(e){hb=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=Ju[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return hb(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,H,_,A,ie,m||0,te),ae}var gq={kernelName:Vs,backendName:"wasm",setupFunc:Aq,kernelFunc:yq},db;function xq(e){db=e.wasm.cwrap(Us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=Ju[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return db(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,H,_,A,ie,m||0,te),ae}var _q={kernelName:Us,backendName:"wasm",setupFunc:xq,kernelFunc:wq},pb;function bq(e){pb=e.wasm.cwrap(no,null,["number","number","number","number","number","number","array","number"])}function vq(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=K1.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return pb(d,Mn[r.dtype],p,i,h,o,m,f),u}var kq={kernelName:no,backendName:"wasm",setupFunc:bq,kernelFunc:vq},fb;function Iq(e){fb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Nq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=mr({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],m=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let f=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(m.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return fb(A,Mn[a.dtype],_,f,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var Sq={kernelName:to,backendName:"wasm",setupFunc:Iq,kernelFunc:Nq},Tq=!1,Eq=un(ro,Tq,"bool"),Cq=!1,Rq=un(hs,Cq,"bool"),mb;function Fq(e){mb=e.wasm.cwrap(ps,null,["number","number","number"])}function Mq(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;mb(a,n,i)}return s}var $q={kernelName:ps,backendName:"wasm",setupFunc:Fq,kernelFunc:Mq},Oq=!1,Dq=un(oo,Oq,"bool"),zq=!1,Pq=un(lo,zq,"bool"),Lq=wn(fs),Wq=!1,Bq=un(co,Wq,"bool"),Ab;function Vq(e){Ab=e.wasm.cwrap(ms,null,["number, number, number"])}function Uq(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=bl(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",c,p);let[m,f]=C.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;Ab(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var Hq={kernelName:ms,backendName:"wasm",setupFunc:Vq,kernelFunc:Uq},jq=!1,Gq=un(As,jq),yb;function qq(e){yb=e.wasm.cwrap(ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Xq(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=C.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,_=c.strideHeight,b=c.strideWidth,w=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Sl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=yr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Sl.className="Sequential";re.registerClass(Sl);function Kee(e){return new na(e)}function Zee(e){return new Sl(e)}function Yee(e,t){return t==null&&(t={}),qee(e,t)}function d3(e){return _3(e)}function Jee(e,t){sr.registerCallbackConstructor(e,t)}var $n=class extends re.Serializable{getConfig(){return{}}},K3=class extends $n{apply(e,t=1){return CJ(e,t)}};K3.className="elu";re.registerClass(K3);var Z3=class extends $n{apply(e){return ud(e)}};Z3.className="selu";re.registerClass(Z3);var Y3=class extends $n{apply(e){return Cr(e)}};Y3.className="relu";re.registerClass(Y3);var J3=class extends $n{apply(e){return B(()=>tl(6,Cr(e)))}};J3.className="relu6";re.registerClass(J3);var Q3=class extends $n{apply(e){return e}};Q3.className="linear";re.registerClass(Q3);var e7=class extends $n{apply(e){return In(e)}};e7.className="sigmoid";re.registerClass(e7);var t7=class extends $n{apply(e){return FJ(e)}};t7.className="hardSigmoid";re.registerClass(t7);var n7=class extends $n{apply(e){return Qo(e)}};n7.className="softplus";re.registerClass(n7);var r7=class extends $n{apply(e){return RJ(e)}};r7.className="softsign";re.registerClass(r7);var a7=class extends $n{apply(e){return Xo(e)}};a7.className="tanh";re.registerClass(a7);var xA=class extends $n{apply(e,t=-1){return Ou(e,t)}};xA.className="softmax";re.registerClass(xA);var s7=class extends $n{apply(e,t=-1){return nd(e,t)}};s7.className="logSoftmax";re.registerClass(s7);var i7=class extends $n{apply(e,t=1){return B(()=>In(e.mul(t)).mul(e))}};i7.className="swish";re.registerClass(i7);function Da(e){return e.getClassName()}function wA(e,t={}){return tc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function za(e){if(e==null){let t={};return t.className="linear",t.config={},wA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},wA(t)}else return e instanceof $n?e:wA(e)}function _A(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var o7=class extends re.Serializable{},pc=class extends o7{constructor(e){super();_A(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=Nt([1]);return this.hasL1&&(t=se(t,Ne(P(this.l1,Mt(e))))),this.hasL2&&(t=se(t,Ne(P(this.l2,ic(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};pc.className="L1L2";re.registerClass(pc);function Qee(e){return _A(e),new pc({l1:e!=null?e.l1:null,l2:0})}function ete(e){return _A(e),new pc({l2:e!=null?e.l2:null,l1:0})}var l7={l1l2:"L1L2"};function lt(e){return Om(e)}function u7(e,t={}){return tc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in l7?l7[e]:e,config:{}};return u7(t)}else return e instanceof o7?e:u7(e)}var bA=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Cr(e);return this.maxValue!=null&&(n=An(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};bA.className="ReLU";re.registerClass(bA);var vA=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Tu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};vA.className="LeakyReLU";re.registerClass(vA);var kA=class extends je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=zt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ot(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(kt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function c7(e,t){return B(()=>(kt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function tte(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=pr()),kt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Xh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Dr(o,n)),o})}function h7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=pr()),kt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=TA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ta.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function nte(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=pr()),kt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=c7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=yf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Dr(o,n)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var EA=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",EA.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Tl(t.kernelSize,e,"kernelSize"),this.strides=Tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,kt(this.dataFormat),this.activation=za(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=zt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=Tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Da(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fc=class extends EA{constructor(e,t){super(e,t);this.kernel=null,fc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=zt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return B(()=>{e=De(e);let n,r=this.bias==null?null:this.bias.read(),a=Zb(this.activation.getClassName());if(a!=null&&this.rank===2)n=h7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=tte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=h7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ot(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},mc=class extends fc{constructor(e){super(2,e);mc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};mc.className="Conv2D";re.registerClass(mc);var Tp=class extends fc{constructor(e){super(3,e);Tp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Tp.className="Conv3D";re.registerClass(Tp);var CA=class extends mc{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ot(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=De(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Sp(o,h,u,this.padding),m=Sp(l,d,c,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let A=Kh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=tt(A,[0,3,1,2])),this.bias!=null&&(A=Dr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Sp(t[r],o,s,this.padding),t[a]=Sp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};CA.className="Conv2DTranspose";re.registerClass(CA);var d7=class extends fc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=zt(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=zt(t.pointwiseConstraint)}build(e){if(e=ot(e),e.length{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=Of(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseConstraint),e.pointwiseConstraint=Dt(this.pointwiseConstraint),e}};d7.className="SeparableConv";var RA=class extends d7{constructor(e){super(2,e)}};RA.className="SeparableConv2D";re.registerClass(RA);var Ep=class extends fc{constructor(e){super(1,e);Ep.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Ep.className="Conv1D";re.registerClass(Ep);var FA=class extends je{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=sp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=sp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};FA.className="Cropping2D";re.registerClass(FA);var MA=class extends je{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};MA.className="UpSampling2D";re.registerClass(MA);function rte(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=pr()),kt(a);let i=TA(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Zo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var $A=class extends EA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=zt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=ot(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=De(e);let n=rte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=gr(t,this.kernelSize[0],this.padding,this.strides[0]),s=gr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseRegularizer),e}};$A.className="DepthwiseConv2D";re.registerClass($A);function p7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function f7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(mr(2,l));if(t=tt(t,u),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Nn(a,-1)),a=tt(a,u)),r&&(t=En(t,0),a!=null&&(a=En(a,0)));let c=[],h,d=n,p=t.shape[0],m=tr(t),f;a!=null&&(f=tr(a));for(let y=0;ye(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=f[y],x=Tn(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),T=d.map((E,M)=>_[1][M].mul(w).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Cn(c,1)),[h,A,d]})}var zr=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new jt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return mr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){rA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new jt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let r=0;rWt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=p7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new jt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Ar){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=f7((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=Nt(e.shape);return t=Ne(t,[1,2]),t=sc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Um(t,[1,n]):t):this.cell.stateSize>1?[Um(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=yr(r,n);return new e(Object.assign(t,{cell:a}))}};zr.className="RNN";re.registerClass(zr);var lc=class extends je{},Rp=class extends lc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0Tn(e),rate:this.dropout,training:r})),0Tn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Or(P(e,s),this.kernel.read()):a=Or(e,this.kernel.read()),this.bias!=null&&(a=Dr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Da(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Rp.className="SimpleRNNCell";re.registerClass(Rp);var OA=class extends zr{constructor(e){e.cell=new Rp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};OA.className="SimpleRNN";re.registerClass(OA);var Fp=class extends lc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0Tn(e),rate:this.dropout,training:n,count:3})),0Tn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};DA.className="GRU";re.registerClass(DA);var Ac=class extends lc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ot(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends ar{apply(i,o){let l=a.apply([s]),u=new op().apply([s]),c=a.apply([s*2]);return i3(i3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Tn(e),rate:this.dropout,training:n,count:4})),0Tn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zA.className="LSTM";re.registerClass(zA);var Cp=class extends lc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{pi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(yr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return aA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sl3(t(),n),i=()=>oc(s,t,r);return!a||a<=1?Wt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Wt(o.clone()))}var ate=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Nt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(a)):this.states_=[Nt(a)];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(a)):this.states_[0]=Nt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let s=0;sWt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=gr(l,r[0],a,s[0],i[0]),h=gr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};m7.className="ConvRNN2D";var Mp=class extends Ac{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Tl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ut(o,"kernelSize")),this.strides=Tl(r||1,2,"strides"),this.strides.forEach(o=>Ut(o,"strides")),this.padding=a||"valid",Gn(this.padding),this.dataFormat=s||"channelsLast",kt(this.dataFormat),this.dilationRate=Tl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ut(o,"dilationRate"))}build(e){var t;e=ot(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ar{apply(c,h){let d=l.apply([u]),p=Er([u]),m=l.apply([u*2]);return Hm([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0Tn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Tn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Zt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,W,U]=Zt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,D),f=this.recurrentConv(f,L),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(c,f)),G=se(P(X,s),P(j,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ate(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Xr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Dr(a,n,this.dataFormat):a}recurrentConv(e,t){return Xr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Mp.className="ConvLSTM2DCell";re.registerClass(Mp);var PA=class extends m7{constructor(e){let t=new Mp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};PA.className="ConvLSTM2D";re.registerClass(PA);var $p=class extends je{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=De(e);if(0l3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$p.className="Dropout";re.registerClass($p);var LA=class extends $p{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};LA.className="SpatialDropout1D";re.registerClass(LA);var WA=class extends je{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=zt(e.kernelConstraint),this.biasConstraint=zt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ot(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ot(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e),r=Zb(this.activation.getClassName()),a;return r!=null?a=Or(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Or(n,this.kernel.read()),this.bias!=null&&(a=Dr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Da(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Dense";re.registerClass(WA);var BA=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ot(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Fa(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=$a(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=mr(1,l).concat([0]);n.push(tt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=tt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(mr(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Nn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Me("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>yc(a,e[s].shape.length)):r=[yc(this.axes,t.shape.length),yc(this.axes,n.shape.length)],this.normalize&&(t=xp(t,r[0]),n=xp(n,r[1])),ste(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[yc(this.axes,e.length),yc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ey.className="Dot";re.registerClass(ey);var ty=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e);return oc(()=>ip(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};ty.className="GaussianNoise";re.registerClass(ty);var ny=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e);return this.rate>0&&this.rate<1?oc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(ip(n.shape,1,r))},()=>n,t.training||!1):n})}};ny.className="GaussianDropout";re.registerClass(ny);var ry=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||De(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return oc(()=>{let r=De(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Na(nl(n),this.rate);o=ac(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>De(e),t.training||!1)}return e})}};ry.className="AlphaDropout";re.registerClass(ry);function gc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=o5(e,t,n,r,a,s);else if(e.rank===3)i=l5(e,t,n,r,a,s);else if(e.rank===4)i=u5(e,t,n,r,a,s);else throw new Me(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function ite(e,t,n,r,a=.001){return B(()=>{let s=ad(e,r),i=s.mean,o=s.variance;return[gc(e,i,o,n,t,a),i,o]})}function ote(e,t,n,r,a=.001){return B(()=>{let s=ad(e,r),i=s.mean,o=s.variance,l=[];for(let p of 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je{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=zt(e.betaConstraint),this.gammaConstraint=zt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=ot(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new jt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,r=De(e),a=r.shape,s=a.length,i=mr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,mr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return gc(r,A,y,g,_,this.epsilon)}else return 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extends je{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ot(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: 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s==="max"?i=Cu(e,t,n,o):i=vu(e,t,n,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function A7(e,t,n,r,a,s){return B(()=>{kt(a),e3(s),Gn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=pr()),s==null&&(s="max"),e=c7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Tf(e,t,n,o):i=ff(e,t,n,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var y7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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kt(a),Gn(r),Op(e,t,n,r,a,"avg")}};ly.className="AveragePooling1D";re.registerClass(ly);var g7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),Gn(this.padding),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},uy=class extends g7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"max")}};uy.className="MaxPooling2D";re.registerClass(uy);var cy=class extends g7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"avg")}};cy.className="AveragePooling2D";re.registerClass(cy);var x7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),Gn(this.padding),this.inputSpec=[new jt({ndim:5})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),r=gr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},hy=class extends x7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),A7(e,t,n,r,a,"max")}};hy.className="MaxPooling3D";re.registerClass(hy);var dy=class extends x7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),A7(e,t,n,r,a,"avg")}};dy.className="AveragePooling3D";re.registerClass(dy);var w7=class extends je{constructor(e){super(e);this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},py=class extends w7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return wt(n,1)})}};py.className="GlobalAveragePooling1D";re.registerClass(py);var fy=class extends w7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return Un(n,1)})}};fy.className="GlobalMaxPooling1D";re.registerClass(fy);var _7=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},my=class extends _7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?wt(n,[1,2]):wt(n,[2,3])})}};my.className="GlobalAveragePooling2D";re.registerClass(my);var Ay=class extends _7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?Un(n,[1,2]):Un(n,[2,3])})}};Ay.className="GlobalMaxPooling2D";re.registerClass(Ay);var b7=class extends je{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=yr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=yr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?hte:e.mergeMode,cte(this.mergeMode),e.weights)throw new Me("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Tl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=wr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Tl.className="Sequential";re.registerClass(Tl);function ete(e){return new ra(e)}function tte(e){return new Tl(e)}function nte(e,t){return t==null&&(t={}),Jee(e,t)}function y3(e){return N3(e)}function rte(e,t){ir.registerCallbackConstructor(e,t)}var $n=class extends re.Serializable{getConfig(){return{}}},e7=class extends $n{apply(e,t=1){return OJ(e,t)}};e7.className="elu";re.registerClass(e7);var t7=class extends $n{apply(e){return cd(e)}};t7.className="selu";re.registerClass(t7);var n7=class extends $n{apply(e){return Fr(e)}};n7.className="relu";re.registerClass(n7);var r7=class extends $n{apply(e){return B(()=>nl(6,Fr(e)))}};r7.className="relu6";re.registerClass(r7);var a7=class extends $n{apply(e){return e}};a7.className="linear";re.registerClass(a7);var s7=class extends $n{apply(e){return In(e)}};s7.className="sigmoid";re.registerClass(s7);var i7=class extends $n{apply(e){return zJ(e)}};i7.className="hardSigmoid";re.registerClass(i7);var o7=class extends $n{apply(e){return el(e)}};o7.className="softplus";re.registerClass(o7);var l7=class extends $n{apply(e){return DJ(e)}};l7.className="softsign";re.registerClass(l7);var u7=class extends $n{apply(e){return Ko(e)}};u7.className="tanh";re.registerClass(u7);var wA=class extends $n{apply(e,t=-1){return Du(e,t)}};wA.className="softmax";re.registerClass(wA);var c7=class extends $n{apply(e,t=-1){return rd(e,t)}};c7.className="logSoftmax";re.registerClass(c7);var h7=class extends $n{apply(e,t=1){return B(()=>In(e.mul(t)).mul(e))}};h7.className="swish";re.registerClass(h7);function za(e){return e.getClassName()}function _A(e,t={}){return nc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Pa(e){if(e==null){let t={};return t.className="linear",t.config={},_A(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},_A(t)}else return e instanceof $n?e:_A(e)}function bA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var d7=class extends re.Serializable{},fc=class extends d7{constructor(e){super();bA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=St([1]);return this.hasL1&&(t=se(t,Ne(P(this.l1,Mt(e))))),this.hasL2&&(t=se(t,Ne(P(this.l2,oc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};fc.className="L1L2";re.registerClass(fc);function ate(e){return bA(e),new fc({l1:e!=null?e.l1:null,l2:0})}function ste(e){return bA(e),new fc({l2:e!=null?e.l2:null,l1:0})}var p7={l1l2:"L1L2"};function lt(e){return Dm(e)}function f7(e,t={}){return nc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in p7?p7[e]:e,config:{}};return f7(t)}else return e instanceof d7?e:f7(e)}var vA=class extends He{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Fr(e);return this.maxValue!=null&&(n=An(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};vA.className="ReLU";re.registerClass(vA);var kA=class extends He{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Eu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};kA.className="LeakyReLU";re.registerClass(kA);var IA=class extends He{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=zt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ot(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(It(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function m7(e,t){return B(()=>(It(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function ite(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=Ar()),It(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Kh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Pr(o,n)),o})}function A7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=Ar()),It(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=EA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ea.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function ote(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=Ar()),It(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=m7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=gf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Pr(o,n)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var CA=class extends He{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",CA.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=El(t.kernelSize,e,"kernelSize"),this.strides=El(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,It(this.dataFormat),this.activation=Pa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=zt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=El(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Dr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Pm(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:za(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},mc=class extends CA{constructor(e,t){super(e,t);this.kernel=null,mc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=zt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. 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Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=De(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Tp(o,h,u,this.padding),m=Tp(l,d,c,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let A=Zh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=tt(A,[0,3,1,2])),this.bias!=null&&(A=Pr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Tp(t[r],o,s,this.padding),t[a]=Tp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv2DTranspose";re.registerClass(RA);var y7=class extends mc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=zt(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=zt(t.pointwiseConstraint)}build(e){if(e=ot(e),e.length{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=Df(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseConstraint),e.pointwiseConstraint=Dt(this.pointwiseConstraint),e}};y7.className="SeparableConv";var FA=class extends y7{constructor(e){super(2,e)}};FA.className="SeparableConv2D";re.registerClass(FA);var Cp=class extends mc{constructor(e){super(1,e);Cp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Pm(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Cp.className="Conv1D";re.registerClass(Cp);var MA=class extends He{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};MA.className="Cropping2D";re.registerClass(MA);var $A=class extends He{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,TJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$A.className="UpSampling2D";re.registerClass($A);function lte(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=Ar()),It(a);let i=EA(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Yo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var OA=class extends CA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=zt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=ot(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=De(e);let n=lte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=_r(t,this.kernelSize[0],this.padding,this.strides[0]),s=_r(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseRegularizer),e}};OA.className="DepthwiseConv2D";re.registerClass(OA);function g7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function x7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(gr(2,l));if(t=tt(t,u),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Nn(a,-1)),a=tt(a,u)),r&&(t=En(t,0),a!=null&&(a=En(a,0)));let c=[],h,d=n,p=t.shape[0],m=nr(t),f;a!=null&&(f=nr(a));for(let y=0;ye(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=f[y],x=Tn(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),T=d.map((E,M)=>_[1][M].mul(w).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Cn(c,1)),[h,A,d]})}var Lr=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Rp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ht({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){aA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ht({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ta("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>St([n,r])):this.states_=[St([n,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>St([n,r])):this.states_[0]=St([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let r=0;rWt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=g7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ht({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof xr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=x7((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=St(e.shape);return t=Ne(t,[1,2]),t=ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Hm(t,[1,n]):t):this.cell.stateSize>1?[Hm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Lr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=wr(r,n);return new e(Object.assign(t,{cell:a}))}};Lr.className="RNN";re.registerClass(Lr);var uc=class extends He{},Fp=class extends uc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0Tn(e),rate:this.dropout,training:r})),0Tn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=zr(P(e,s),this.kernel.read()):a=zr(e,this.kernel.read()),this.bias!=null&&(a=Pr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,zr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Fp.className="SimpleRNNCell";re.registerClass(Fp);var DA=class extends Lr{constructor(e){e.cell=new Fp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};DA.className="SimpleRNN";re.registerClass(DA);var Mp=class extends uc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0Tn(e),rate:this.dropout,training:n,count:3})),0Tn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zA.className="GRU";re.registerClass(zA);var yc=class extends uc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ot(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends sr{apply(i,o){let l=a.apply([s]),u=new lp().apply([s]),c=a.apply([s*2]);return h3(h3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Tn(e),rate:this.dropout,training:n,count:4})),0Tn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};PA.className="LSTM";re.registerClass(PA);var Rp=class extends uc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{fi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(wr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return sA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sp3(t(),n),i=()=>lc(s,t,r);return!a||a<=1?Wt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Wt(o.clone()))}var ute=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=St(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ta("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>St(a)):this.states_=[St(a)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>St(a)):this.states_[0]=St(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). 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Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends sr{apply(c,h){let d=l.apply([u]),p=Rr([u]),m=l.apply([u*2]);return Gm([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0Tn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Tn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Zt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,W,U]=Zt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,D),f=this.recurrentConv(f,L),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(c,f)),G=se(P(X,s),P(H,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ute(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Kr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Pr(a,n,this.dataFormat):a}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};$p.className="ConvLSTM2DCell";re.registerClass($p);var LA=class extends w7{constructor(e){let t=new $p(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};LA.className="ConvLSTM2D";re.registerClass(LA);var Op=class extends He{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=De(e);if(0p3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Op.className="Dropout";re.registerClass(Op);var WA=class extends Op{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};WA.className="SpatialDropout1D";re.registerClass(WA);var BA=class extends He{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=zt(e.kernelConstraint),this.biasConstraint=zt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ot(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ot(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e),r=t3(this.activation.getClassName()),a;return r!=null?a=zr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=zr(n,this.kernel.read()),this.bias!=null&&(a=Pr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};BA.className="Dense";re.registerClass(BA);var VA=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ot(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Ma(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Oa(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=gr(1,l).concat([0]);n.push(tt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=tt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(gr(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Nn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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Array.isArray(this.axes)?r=this.axes.map((a,s)=>gc(a,e[s].shape.length)):r=[gc(this.axes,t.shape.length),gc(this.axes,n.shape.length)],this.normalize&&(t=wp(t,r[0]),n=wp(n,r[1])),cte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[gc(this.axes,e.length),gc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ty.className="Dot";re.registerClass(ty);var ny=class extends 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He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=zt(e.betaConstraint),this.gammaConstraint=zt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=ot(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ht({ndim:e.length,axes:{[t]:n}})];let 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t=_r(t,this.poolSize[0],this.padding,this.strides[0]),n=_r(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},cy=class extends v7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),Dp(e,t,n,r,a,"max")}};cy.className="MaxPooling2D";re.registerClass(cy);var hy=class extends v7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),Dp(e,t,n,r,a,"avg")}};hy.className="AveragePooling2D";re.registerClass(hy);var k7=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),qn(this.padding),this.inputSpec=[new Ht({ndim:5})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=_r(t,this.poolSize[0],this.padding,this.strides[0]),n=_r(n,this.poolSize[1],this.padding,this.strides[1]),r=_r(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},dy=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),_7(e,t,n,r,a,"max")}};dy.className="MaxPooling3D";re.registerClass(dy);var py=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),_7(e,t,n,r,a,"avg")}};py.className="AveragePooling3D";re.registerClass(py);var I7=class extends He{constructor(e){super(e);this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},fy=class extends I7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return wt(n,1)})}};fy.className="GlobalAveragePooling1D";re.registerClass(fy);var my=class extends I7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return Hn(n,1)})}};my.className="GlobalMaxPooling1D";re.registerClass(my);var N7=class extends He{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ay=class extends N7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?wt(n,[1,2]):wt(n,[2,3])})}};Ay.className="GlobalAveragePooling2D";re.registerClass(Ay);var yy=class extends N7{call(e,t){return B(()=>{let n=De(e);return 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r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[nt(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Zs(r,fe(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[Zs(s,fe(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Sa(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Sa(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:H(l,s)});return[Cn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return tr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Ia(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Zt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[O5(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[D5(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[Uf(r,s,a,s.dtype===i.dtype?i:fe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_ne=(e,t,n)=>{switch(e.op){case"FFT":return[Du(k("x",e,t,n))];case"IFFT":return[rl(k("x",e,t,n))];case"RFFT":return[zu(k("x",e,t,n))];case"IRFFT":return[fd(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bne=(e,t,n)=>{switch(e.op){case"Cast":return[fe(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[Nn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Sa(k("x",e,t,n),r)]}case"Reshape":return[H(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[Ef(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Kr(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[Ru(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[ku(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[gf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Iu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ev(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>ene(s,i,o));case"basic_math":return B(()=>tne(s,i,o));case"control":return one(s,i,o);case"convolution":return B(()=>lne(s,i,o));case"creation":return B(()=>une(s,i,o));case"dynamic":return cne(s,i,o);case"evaluation":return B(()=>hne(s,i,o));case"image":return B(()=>mne(s,i,o));case"graph":return B(()=>dne(s,i,o));case"logical":return B(()=>Ane(s,i,o));case"matrices":return B(()=>yne(s,i,o));case"normalization":return B(()=>gne(s,i,o));case"reduction":return B(()=>xne(s,i,o));case"slice_join":return B(()=>wne(s,i,o));case"spectral":return B(()=>_ne(s,i,o));case"transformation":return B(()=>bne(s,i,o));case"hash_table":return fne(s,i,o,r);case"custom":let l=C7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Qte(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var tv=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function rv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((nv(d)||vne(d)||kne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ine(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Nne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Sne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Tne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function nv(e){return Nne.indexOf(e.op)>=0}function vne(e){return Sne.indexOf(e.op)>=0}function kne(e){return Tne.indexOf(e.op)>=0}var My=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new My(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=rv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Ine(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new tv(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=On(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;mvn(m,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=$te(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new tv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>vn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=rv(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=On(g),w=[];w[b]=e[g],p[_]=w});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!nv(g)&&!vn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=ra(c.node.name,n)),r[c.node.name]==null){let d=ev(c.node,r,n,this._resourceManager);h||([h]=ra(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ra(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ene=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Cne="?tfjs-format=file",Rne="model.json",av=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ene}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=mn.browserHTTPRequest(e,this.loadOptions);else{let t=mn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(mn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=mn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new My(K7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=K7.Instance.transformGraph(e.modelInitializer);this.initializer=new My(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=mn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Xe)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Tt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Rne}${Cne}`);let n=new av(e,t);return await n.load(),n}var Fne="3.2.0",sv={};Oe(sv,{CSVDataset:()=>ov,Dataset:()=>El,FileDataSource:()=>lv,TextLineDataset:()=>iv,URLDataSource:()=>uv,array:()=>Mne,csv:()=>One,func:()=>Dne,generator:()=>zne,microphone:()=>Lne,version_data:()=>Wne,webcam:()=>Pne,zip:()=>$ne});var Bne=Mi(Y2()),Vne=Mi(Y2());function Une(e,t){return Lp(e,t)}function Lp(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Cl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=Lp(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else 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Ht{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},tre=class extends Ht{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},rre=class extends Ht{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},are=class extends Ht{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Se(e.value)}}},sre=class extends Ht{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=lr.getTensorsInContainer(e.value),n=this.transform(e.value),r=lr.getTensorsInContainer(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},ire=class extends Ht{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Av=class extends Ht{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=lr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=lr.getTensorsInContainer(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Dy=class extends Ht{constructor(){super();this.outputQueue=new $y,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},ore=class extends Dy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=lr.getTensorsInContainer(e.value),n=this.transform(e.value),r=lr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return!0}},mv=class extends Ht{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},La;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(La||(La={}));var Jne=class extends Ht{constructor(e,t=La.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Ht?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await dv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case La.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new wv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),ur(n,t)}},_v=class extends Ht{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Bt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=gn([s,a,o,i],[1,4])}else this.cropBox=gn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new _v(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Uo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=Nn(fe(e,"float32"),0),n;n=Ke.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},bv=class{},vv=class extends Ht{split(e){return new hre(this,e)}},hre=class extends vv{constructor(e,t){super();this.upstream=e,this.impl=new dre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dre=class extends Dy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},fre=class extends Ht{decodeUTF8(){return new pre(this)}},pre=class extends vv{constructor(e){super();this.upstream=e,this.impl=new mre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mre=class extends Dy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=y8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},kv=class extends fre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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bv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Iv(this.url)?new lv(this.url,this.fileOptions).iterator():yre(this.url,this.fileOptions)}};function One(e,t={}){return new ov(new uv(e),t)}function Dne(e){let t=Oy(e);return Dn(async()=>t)}function zne(e){return Dn(async()=>{let t=await e();return Oy(()=>t.next())})}async function Pne(e,t){return _v.create(e,t)}async function Lne(e){return wv.create(e)}var Wne="3.2.0",gre={tfjs:g8,"tfjs-core":x8,"tfjs-data":w8,"tfjs-layers":_8,"tfjs-converter":b8,"tfjs-backend-cpu":Nx,"tfjs-backend-webgl":qw,"tfjs-backend-wasm":zb};var zn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Nv(){if(!nf(zn.name)){Te("backend registration:",zn.name);try{zn.canvas=typeof OffscreenCanvas!="undefined"?new 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r=Ee(e,[0,1],[-1,2]),a=se(r,t),s=Ee(e,[0,3],[-1,2]),i=we(s,n),o=we(a,n),l=we(i,2),u=me(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return Ko([h,d],1)}var Tv=class{constructor(t,n){this.blazeFaceModel=t,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=xre(n.face.detector.inputSize),this.anchors=gn(this.anchorsData),this.inputSize=Bt([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let h=t.resizeBilinear([this.width,this.height]),d=me(h.div(127.5),1),p=this.blazeFaceModel.predict(d),m;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),_=nt([g[0],g[2]],2),b=nt([g[1],g[3]],2);m=nt([b,_],1).squeeze(0)}else m=p.squeeze();let f=_re(m,this.anchors,this.inputSize),A=Ee(m,[0,0],[-1,1]),y=In(A).squeeze();return[m,f,y]}),s=await Ke.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Ee(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;hthis.config.face.detector.minConfidence){let m=wre(l[h]),f=this.anchorsData[d],A=B(()=>Ee(n,[d,Sv-1],[1,-1]).squeeze().reshape([Sv,-1]));c.push({box:m,landmarks:A,anchor:f,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),n.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.width,t.shape[1]/this.height]}}};async function Ev(e){let t=await Tt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new Tv(t,e);return e.debug&&Te(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function Cv(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function 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Zy={};_r(Zy,{FaceBoxes:()=>Yy,load:()=>zre});var Ky={};function Wr(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Ky[e]=i,Te("Human profiler",e,i)}var Yy=class{constructor(t,n){this.enlarge=1.1,this.model=t,this.config=n}async estimateFaces(t,n){n&&(this.config=n);let r=[],a=Ke.resizeBilinear(t,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),s=a.toInt(),i,o;if(n.profile){let l=await 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t2={};_r(t2,{load:()=>s2,predict:()=>i2});var wi,n2={gender:""},Xp=Number.MAX_SAFE_INTEGER,r2=!1,a2=[.2989,.587,.114];async function s2(e){return wi||(wi=await Tt(e.face.gender.modelPath),r2=wi.inputs[0].shape[3]===1,e.debug&&Te(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),wi}async function i2(e,t){return wi?Xp{let r=Ke.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;r2?a=B(()=>{let[o,l,u]=Zt(r,3,3),c=P(o,a2[0]),h=P(l,a2[1]),d=P(u,a2[2]);return Go([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Se(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await wi.predict(a));else{let o=t.face.gender.enabled?await cr(()=>wi.predict(a)):{};s=o.result.clone(),o.result.dispose(),Wr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(r2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let 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Yv(t.heatmapScores,t.offsets,n)],l=k2(o,[a,s],[n.body.inputSize,n.body.inputSize]);r(l)})}var N2=class{constructor(t){this.baseModel=t}async estimatePoses(t,n){let r=t6(t,[n.body.inputSize,n.body.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await rae(t,a,n):await nae(t,a,n);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function S2(e){let t=await Tt(e.body.modelPath),n=new f2(t);return e.debug&&Te(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new N2(n)}var F2={};_r(F2,{HandPose:()=>$2,load:()=>O2});function Qp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function kc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function n6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ke.cropAndResize(t,s,[0],n)}function r6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function e0(e,t=1.5){let n=kc(e),r=Qp(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function t0(e){let t=kc(e),n=Qp(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var T2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=gn(this.anchors),this.inputSizeTensor=Bt([n,n]),this.doubleInputSizeTensor=Bt([n*2,n*2])}normalizeBoxes(t){return B(()=>{let n=Ee(t,[0,0],[-1,2]),r=Ee(t,[0,2],[-1,2]),a=se(we(n,this.inputSizeTensor),this.anchorsTensor),s=we(r,this.doubleInputSizeTensor),i=P(me(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return Ko([i,o],1)})}normalizeLandmarks(t,n){return B(()=>{let r=se(we(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=B(()=>In(Ee(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Ee(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let u=await Ke.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),c=u.arraySync();s.dispose(),u.dispose();let h=[];for(let d of c)if(i[d]>=n.hand.minConfidence){let p=Ee(l,[d,0],[1,-1]),m=Ee(a,[d,5],[1,14]),f=B(()=>this.normalizeLandmarks(m,d).reshape([-1,2]));m.dispose(),h.push({box:p,palmLandmarks:f,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=B(()=>t.resizeBilinear([n.hand.inputSize,n.hand.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),c=u.slice(0,2),h=u.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(r6({startPoint:c,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/n.hand.inputSize,r/n.hand.inputSize]))}return o}};function aae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function a6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return aae(n)}var s6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ba(e,t){let n=0;for(let r=0;rC2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return e0(t0(a),iae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=e0(t0(n),l6);r.palmLandmarks=[];for(let a=0;a[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=E2(r,[0,0]),u=o.map(p=>[...C2(p,l),p[2]]),c=o6(a),h=[...kc(n),1],d=[Ba(h,c[0]),Ba(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i=n.hand.minConfidence){let _=H(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let N={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=e0(t0(o),l6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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R2(r,n,e.hand.inputSize),s=new $2(a);return e.hand.enabled&&e.debug&&Te(`load model: ${e.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),e.hand.landmarks&&e.debug&&Te(`load model: ${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var h6=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},d6=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},p6=e=>{if(!e)return[];let t=[];for(let n=0;n{if(!e)return[];let t=[];for(let n=0;n0){let a=r.reduce((i,o)=>i.position[2]i.position[1](u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let 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r=k("image",e,t,n),a=k("boxes",e,t,n),s=k("boxInd",e,t,n),i=k("cropSize",e,t,n),o=k("method",e,t,n),l=k("extrapolationValue",e,t,n);return[je.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_ne=(e,t,n)=>{switch(e.op){case"Equal":return[Ia(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[ei(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[er(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[Sa(k("a",e,t,n),k("b",e,t,n))];case"Less":return[ed(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[Js(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[tr(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[Cu(k("a",e,t,n))];case"LogicalOr":return[ad(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[yn(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bne=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ue(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[tt(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=k("args",e,t,n);return[Ea.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vne=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Zs(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[Zs(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[If(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[Du(k("x",e,t,n))];case"LogSoftmax":return[rd(k("x",e,t,n))];case"SparseToDense":return[Hf(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kne=(e,t,n)=>{switch(e.op){case"Max":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Hn(k("x",e,t,n),i,o)]}case"Mean":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[wt(k("x",e,t,n),i,o)]}case"Min":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[tl(k("x",e,t,n),i,o)]}case"Sum":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Ne(k("x",e,t,n),i,o)]}case"All":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[qh(k("x",e,t,n),i,o)]}case"Any":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[_u(k("x",e,t,n),i,o)]}case"ArgMax":{let i=k("axis",e,t,n);return[bu(k("x",e,t,n),i)]}case"ArgMin":{let i=k("axis",e,t,n);return[of(k("x",e,t,n),i)]}case"Prod":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[id(k("x",e,t,n),i,o)]}case"Cumsum":{let i=k("axis",e,t,n),o=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[Jh(k("x",e,t,n),i,o,l)]}case"Bincount":let r=k("x",e,t,n),a=k("weights",e,t,n),s=k("size",e,t,n);return[m5(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),u=k("binaryOutput",e,t,n);return[w5(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ine=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[nt(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Ys(r,fe(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[Ys(s,fe(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Ta(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Ta(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:j(l,s)});return[Cn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return nr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Na(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Zt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[W5(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[B5(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[Hf(r,s,a,s.dtype===i.dtype?i:fe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nne=(e,t,n)=>{switch(e.op){case"FFT":return[zu(k("x",e,t,n))];case"IFFT":return[al(k("x",e,t,n))];case"RFFT":return[Pu(k("x",e,t,n))];case"IRFFT":return[md(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Sne=(e,t,n)=>{switch(e.op){case"Cast":return[fe(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[Nn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Ta(k("x",e,t,n),r)]}case"Reshape":return[j(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[Cf(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Zr(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[Fu(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[Iu(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[xf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Nu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function sv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>sne(s,i,o));case"basic_math":return B(()=>ine(s,i,o));case"control":return dne(s,i,o);case"convolution":return B(()=>pne(s,i,o));case"creation":return B(()=>fne(s,i,o));case"dynamic":return mne(s,i,o);case"evaluation":return B(()=>Ane(s,i,o));case"image":return B(()=>wne(s,i,o));case"graph":return B(()=>yne(s,i,o));case"logical":return B(()=>_ne(s,i,o));case"matrices":return B(()=>bne(s,i,o));case"normalization":return B(()=>vne(s,i,o));case"reduction":return B(()=>kne(s,i,o));case"slice_join":return B(()=>Ine(s,i,o));case"spectral":return B(()=>Nne(s,i,o));case"transformation":return B(()=>Sne(s,i,o));case"hash_table":return xne(s,i,o,r);case"custom":let l=O7(s.op);if(l&&l.customExecutor)return l.customExecutor(new ane(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function lv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((ov(d)||Tne(d)||Ene(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Cne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Rne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Fne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Mne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function ov(e){return Rne.indexOf(e.op)>=0}function Tne(e){return Fne.indexOf(e.op)>=0}function Ene(e){return Mne.indexOf(e.op)>=0}var $y=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new $y(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=lv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. 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Missing the following inputs: [${r}]`)}return Cne(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new iv(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=On(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;mvn(m,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Lte(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new iv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>vn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=lv(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=On(g),w=[];w[b]=e[g],p[_]=w});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!ov(g)&&!vn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=aa(c.node.name,n)),r[c.node.name]==null){let d=sv(c.node,r,n,this._resourceManager);h||([h]=aa(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=aa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},$ne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},One="?tfjs-format=file",Dne="model.json",uv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new $ne}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=mn.browserHTTPRequest(e,this.loadOptions);else{let t=mn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(mn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=mn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new $y(ev.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=ev.Instance.transformGraph(e.modelInitializer);this.initializer=new $y(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=mn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ke)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function kt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Dne}${One}`);let n=new uv(e,t);return await n.load(),n}var zne="3.2.0",cv={};Oe(cv,{CSVDataset:()=>dv,Dataset:()=>Cl,FileDataSource:()=>pv,TextLineDataset:()=>hv,URLDataSource:()=>fv,array:()=>Pne,csv:()=>Wne,func:()=>Bne,generator:()=>Vne,microphone:()=>Hne,version_data:()=>jne,webcam:()=>Une,zip:()=>Lne});var Gne=$i(ng()),qne=$i(ng());function Xne(e,t){return Wp(e,t)}function Wp(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Rl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=Wp(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Kne(e,t=Av){return mv(e,t)}function mv(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Rl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(u=>u[i]),l=mv(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function Av(e){return e===null?null:Rl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function yv(e,t){let n=new Map;Wp(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return Wp(e,t,n)}function Rl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ke))}function Yne(e){return e==null||Zne(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ke||v.isTypedArray(e)}function Zne(e){return e===null||typeof e!="object"&&typeof e!="function"}function Qne(e){return Xne(e,Jne)}function Jne(e){return e instanceof Ke?{value:e.clone(),recurse:!1}:Rl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var gv=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},Oy=class extends gv{constructor(){super(Oy.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new lre(this,e,t)}columnMajorBatch(e,t=!0,n=Av){return this.rowMajorBatch(e,t).map(r=>Kne(r,n))}concatenate(e,t){return new wv(xv([this,e]),t)}take(e){return e<0||e==null?this:new ore(this,e)}skip(e){return e<0||e==null?this:new ire(this,e)}prefetch(e){return new bv(this,e)}shuffle(e,t){return new pre(this,e,t)}serial(){return new sre(this)}},ere=class extends jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Qne(e),done:!1}}},tre=class extends jt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},sre=class extends jt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},ire=class extends jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},lre=class extends jt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},ure=class extends jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},cre=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},hre=class extends jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},_v=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},zy=class extends jt{constructor(){super();this.outputQueue=new Oy,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},dre=class extends zy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return!0}},wv=class extends jt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Wa;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Wa||(Wa={}));var rre=class extends jt{constructor(e,t=Wa.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await yv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Wa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Wa.SHORTEST:return{value:null,done:!0};case Wa.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},bv=class extends jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new gv(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},pre=class extends bv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qne.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Cl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Dn(async()=>(await n.iterator()).columnMajorBatch(e,t,fre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Dn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Dn(async()=>(await t.iterator()).filter(r=>B(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Dn(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Dn(async()=>{let r=Dy(async()=>({value:await t.iterator(),done:!1}));return nre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Gne.alea(t||v.now().toString());return Dn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Dn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Cl.MAX_BUFFER_SIZE=1e4;function Dn(e,t=null){return new class extends Cl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Pne(e){return Dn(async()=>xv(e),e.length)}function Lne(e){if(!Rl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await yv(e,r=>{if(r instanceof Cl)return{value:r.iterator(),recurse:!1};if(Rl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return are(n,Wa.SHORTEST)},t)}function fre(e){if(e===null)return null;let t=e[0];return Yne(t)?{value:mre(e),recurse:!1}:{value:null,recurse:!0}}function mre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ke?Cn(e):pr(e)}var hv=class extends Cl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bp='"',bc=Symbol("out"),vv=Symbol("field"),Vp=Symbol("quote"),Py=Symbol("quoteafterquote"),kv=Symbol("quoteinquote"),dv=class extends Cl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new hv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Iv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),pr(n,t)}},Nv=class extends jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Bt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=gn([s,a,o,i],[1,4])}else this.cropBox=gn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Nv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ho.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=Nn(fe(e,"float32"),0),n;n=je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Sv=class{},Tv=class extends jt{split(e){return new Are(this,e)}},Are=class extends Tv{constructor(e,t){super();this.upstream=e,this.impl=new yre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yre=class extends zy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},xre=class extends jt{decodeUTF8(){return new gre(this)}},gre=class extends Tv{constructor(e){super();this.upstream=e,this.impl=new wre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wre=class extends zy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=b8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Ev=class extends xre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function bre(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=_re(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new Ev(s,t)}else throw new Error(a.statusText)}var _re=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Cv(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var pv=class extends Sv{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Cv(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Ev(this.input,this.options)}},fv=class extends Sv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Cv(this.url)?new pv(this.url,this.fileOptions).iterator():bre(this.url,this.fileOptions)}};function Wne(e,t={}){return new dv(new fv(e),t)}function Bne(e){let t=Dy(e);return Dn(async()=>t)}function Vne(e){return Dn(async()=>{let t=await e();return Dy(()=>t.next())})}async function Une(e,t){return Nv.create(e,t)}async function Hne(e){return Iv.create(e)}var jne="3.2.0",vre={tfjs:v8,"tfjs-core":k8,"tfjs-data":I8,"tfjs-layers":N8,"tfjs-converter":S8,"tfjs-backend-cpu":Rx,"tfjs-backend-webgl":Jw,"tfjs-backend-wasm":Vb};var zn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Rv(){if(!rf(zn.name)){Se("backend registration:",zn.name);try{zn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(zn.width,zn.height):document.createElement("canvas")}catch(e){Se("error: cannot create canvas:",e);return}try{zn.gl=zn.canvas.getContext("webgl2",zn.webGLattr)}catch(e){Se("error: cannot get WebGL2 context:",e);return}try{Wd(2,zn.gl)}catch(e){Se("error: cannot set WebGL2 context:",e);return}try{let e=new Hd(zn.gl);Go(zn.name,()=>new gl(e),zn.priority)}catch(e){Se("error: cannot register WebGL backend:",e);return}try{Po("webgl").forEach(t=>{let n={...t,backendName:zn.name};Hs(n)})}catch(e){Se("error: cannot update WebGL backend registration:",e);return}try{Xa.set("WEBGL_VERSION",2)}catch(e){Se("error: cannot set WebGL backend flags:",e);return}Se("backend registered:",zn.name)}}var Fv=6;function kre(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r({startEndTensor:e,startPoint:Ee(e,[0,0],[-1,2]),endPoint:Ee(e,[0,2],[-1,2])});function Nre(e,t,n){let r=Ee(e,[0,1],[-1,2]),a=se(r,t),s=Ee(e,[0,3],[-1,2]),i=ye(s,n),o=ye(a,n),l=ye(i,2),u=me(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return Zo([h,d],1)}var Mv=class{constructor(t,n){this.blazeFaceModel=t,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=kre(n.face.detector.inputSize),this.anchors=gn(this.anchorsData),this.inputSize=Bt([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let h=t.resizeBilinear([this.width,this.height]),d=me(h.div(127.5),1),p=this.blazeFaceModel.predict(d),m;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),_=nt([g[0],g[2]],2),b=nt([g[1],g[3]],2);m=nt([b,_],1).squeeze(0)}else m=p.squeeze();let f=Nre(m,this.anchors,this.inputSize),A=Ee(m,[0,0],[-1,1]),y=In(A).squeeze();return[m,f,y]}),s=await je.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Ee(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;hthis.config.face.detector.minConfidence){let m=Ire(l[h]),f=this.anchorsData[d],A=B(()=>Ee(n,[d,Fv-1],[1,-1]).squeeze().reshape([Fv,-1]));c.push({box:m,landmarks:A,anchor:f,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),n.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.width,t.shape[1]/this.height]}}};async function $v(e){let t=await kt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new Mv(t,e);return e.debug&&Se(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function Ov(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function vc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function kc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Ly(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return je.cropAndResize(t,s,[0],n)}function Up(e,t=1.6){let n=kc(e),r=vc(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function Hp(e){let t=kc(e),n=vc(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}var jp=[[1,0,0],[0,1,0],[0,0,1]];function Sre(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Dv(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Sre(n)}function zv(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Ba(e,t){let n=0;for(let 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i=0;i{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=B(()=>this.storedBoxes.map((i,o)=>{let l,u=0,c;if(n.face.detector.rotation){let[w,x]=i.landmarks.length>=Fre?$re:zre;u=Dv(i.landmarks[w],i.landmarks[x]);let N=kc({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=je.rotateWithOffset(t,u,0,T);c=Wy(-u,N),l=Ly({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshHeight,this.meshWidth]).div(255)}else{c=jp;let w=t.clone();l=Ly({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,confidence:i.confidence,image:l};let[,h,d]=this.meshDetector.predict(l),p=h.dataSync()[0];if(pi!==null),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var B6=Yc(Uv());var Yy={};hr(Yy,{FaceBoxes:()=>Jy,load:()=>Vre});var Zy={};function lr(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Zy[e]=i,Se("Human profiler",e,i)}var Jy=class{constructor(t,n){this.enlarge=1.1,this.model=t,this.config=n}async estimateFaces(t,n){n&&(this.config=n);let r=[],a=je.resizeBilinear(t,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),s=a.toInt(),i,o;if(n.profile){let l=await 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n2={};hr(n2,{load:()=>i2,predict:()=>o2});var _i,r2={gender:""},Kp=Number.MAX_SAFE_INTEGER,a2=!1,s2=[.2989,.587,.114];async function i2(e){return _i||(_i=await kt(e.face.gender.modelPath),a2=_i.inputs[0].shape[3]===1,e.debug&&Se(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),_i}async function o2(e,t){return _i?Kp{let r=je.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;a2?a=B(()=>{let[o,l,u]=Zt(r,3,3),c=P(o,s2[0]),h=P(l,s2[1]),d=P(u,s2[2]);return qo([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Te(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await _i.predict(a));else{let o=t.face.gender.enabled?await Vn(()=>_i.predict(a)):{};s=o.result.clone(),o.result.dispose(),lr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(a2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let 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a=0;a[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=C2(r,[0,0]),u=o.map(p=>[...R2(p,l),p[2]]),c=p6(a),h=[...Ic(n),1],d=[Va(h,c[0]),Va(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i=n.hand.minConfidence){let _=j(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let N={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else 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kt(e.body.modelPath),ur.width=parseInt(ur.signature.inputs["input_1:0"].tensorShape.dim[2].size),ur.height=parseInt(ur.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Se(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),ur}async function L2(e,t){if(!ur||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=je.resizeBilinear(e,[ur.width||t.body.inputSize,ur.height||t.body.inputSize],!1),a=ye(r,[255]);r.dispose();let s;if(t.profile){let o=await Vn(()=>ur.predict(a));s=o.result.find(l=>l.size===195).dataSync(),o.result.forEach(l=>l.dispose()),lr("blazepose",o)}else{let o=await ur.predict(a);s=o.find(l=>l.size===195||l.size===155).dataSync(),o.forEach(l=>l.dispose())}a.dispose();let i=[];for(let o=0;o{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},g6=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let 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e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Ic(s,i):n[a]=i}),n),{})}var z6=class{constructor(t={}){this.tf=Yc,this.package=D2,this.version=z2,this.config=Ic(y6,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=D6,this.age=Jy,this.gender=t2,this.emotion=o2,this.body=I2,this.hand=F2}profile(){return this.config.profile?Ky:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Te(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?Wv(t,n):0}async load(t=null){this.state="load";let n=ut();t&&(this.config=Ic(this.config,t)),this.firstRun&&(this.config.debug&&Te(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Te("configuration:",this.config),this.config.debug&&Te("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Zy:D6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Qy(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?s2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?c2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?d2(this.config):null),this.models.posenet||(this.config.body.enabled?S2(this.config):null),this.models.handpose||(this.config.hand.enabled?O2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Qy(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await s2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await c2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await d2(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await S2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await O2(this.config))),this.firstRun&&(this.config.debug&&Te("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(ut()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ut();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Te("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Te("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Te("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Nv();try{await this.tf.setBackend(this.config.backend)}catch(r){Te("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Te("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Te(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ut()-n)}}async detectFace(t){var u,c,h,d,p,m;let n,r,a,s,i,o=[];this.state="run:face",n=ut();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));this.perf.face=Math.trunc(ut()-n);for(let f of l){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Te("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?e2(f.image,this.config):{}:(this.state="run:age",n=ut(),r=this.config.face.age.enabled?await e2(f.image,this.config):{},this.perf.age=Math.trunc(ut()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?i2(f.image,this.config):{}:(this.state="run:gender",n=ut(),a=this.config.face.gender.enabled?await i2(f.image,this.config):{},this.perf.gender=Math.trunc(ut()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?h2(f.image,this.config):{}:(this.state="run:emotion",n=ut(),s=this.config.face.emotion.enabled?await h2(f.image,this.config):{},this.perf.emotion=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?p2(f.image,this.config):[]:(this.state="run:embedding",n=ut(),i=this.config.face.embedding.enabled?await p2(f.image,this.config):[],this.perf.embedding=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((c=f==null?void 0:f.annotations)==null?void 0:c.leftEyeIris)&&((h=f==null?void 0:f.annotations)==null?void 0:h.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let A=((d=f.annotations)==null?void 0:d.leftEyeIris)&&((p=f.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;o.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(m=f.image)==null||m.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async detect(t,n={}){return new Promise(async r=>{var d,p,m,f;this.state="config";let a;this.config=Ic(this.config,n),this.state="check";let s=this.sanity(t);s&&(Te(s,t),r({error:s}));let i,o,l,u=ut();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=ut();let c=A6(t,this.config);if(!c||!c.tensor){Te("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ut()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(c.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ut(),l=this.config.face.enabled?await this.detectFace(c.tensor):[],this.perf.face=Math.trunc(ut()-a)),this.analyze("Start Body:"),this.config.async?(i=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(c.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ut(),i=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(c.tensor,this.config)):[],this.perf.body=Math.trunc(ut()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(m=this.models.handpose)==null?void 0:m.estimateHands(c.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ut(),o=this.config.hand.enabled?await((f=this.models.handpose)==null?void 0:f.estimateHands(c.tensor,this.config)):[],this.perf.hand=Math.trunc(ut()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),c.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ut(),h=[...d6(l),...h6(i),...f6(o),...p6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ut()-a)),this.perf.total=Math.trunc(ut()-u),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:c.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(n0);break;case"full":n=await t(r0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+n0;break;case"full":r=1200,n="data:image/jpeg;base64,"+r0;break;default:n=null}let a=new Image(r,r);a.onload=()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=r,s.height=r;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=i==null?void 0:i.getImageData(0,0,r,r);this.detect(o,this.config).then(l=>t(l))},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(n0):t(r0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s}async warmup(t){let n=ut();t&&(this.config=Ic(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=ut();return this.config.debug&&Te("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};export{z6 as default}; +2Q==`;var B2={};hr(B2,{author:()=>C6,browser:()=>T6,bugs:()=>R6,default:()=>yae,dependencies:()=>D6,description:()=>k6,devDependencies:()=>P6,engines:()=>$6,homepage:()=>F6,keywords:()=>W6,license:()=>M6,main:()=>N6,module:()=>S6,name:()=>v6,peerDependencies:()=>z6,repository:()=>O6,scripts:()=>L6,sideEffects:()=>I6,types:()=>E6,version:()=>V2});var v6="@vladmandic/human",V2="0.40.0",k6="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",I6=!1,N6="dist/human.node.js",S6="dist/human.esm.js",T6="dist/human.esm.js",E6="types/human.d.ts",C6="Vladimir Mandic ",R6={url:"https://github.com/vladmandic/human/issues"},F6="https://github.com/vladmandic/human#readme",M6="MIT",$6={node:">=12.0.0"},O6={type:"git",url:"git+https://github.com/vladmandic/human.git"},D6={},z6={},P6={"@tensorflow/tfjs":"^3.2.0","@tensorflow/tfjs-backend-cpu":"^3.2.0","@tensorflow/tfjs-backend-wasm":"^3.2.0","@tensorflow/tfjs-backend-webgl":"^3.2.0","@tensorflow/tfjs-converter":"^3.2.0","@tensorflow/tfjs-core":"^3.2.0","@tensorflow/tfjs-data":"^3.2.0","@tensorflow/tfjs-layers":"^3.2.0","@tensorflow/tfjs-node":"^3.2.0","@tensorflow/tfjs-node-gpu":"^3.2.0","@types/node":"^14.14.31","@typescript-eslint/eslint-plugin":"^4.16.1","@typescript-eslint/parser":"^4.16.1","@vladmandic/pilogger":"^0.2.14",chokidar:"^3.5.1",dayjs:"^1.10.4",esbuild:"^0.8.54",eslint:"^7.21.0","eslint-config-airbnb-base":"^14.2.1","eslint-plugin-import":"^2.22.1","eslint-plugin-json":"^2.1.2","eslint-plugin-node":"^11.1.0","eslint-plugin-promise":"^4.3.1",rimraf:"^3.0.2",seedrandom:"^3.0.5","simple-git":"^2.35.2",tslib:"^2.1.0",typescript:"^4.3.0-dev.20210302"},L6={start:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",lint:"eslint src demo server",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",update:"npm update --depth 20 --force && npm dedupe && npm prune && npm audit"},W6=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],yae={name:v6,version:V2,description:k6,sideEffects:I6,main:N6,module:S6,browser:T6,types:E6,author:C6,bugs:R6,homepage:F6,license:M6,engines:$6,repository:O6,dependencies:D6,peerDependencies:z6,devDependencies:P6,scripts:L6,keywords:W6};var ut=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Nc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Nc(s,i):n[a]=i}),n),{})}var V6=class{constructor(t={}){this.tf=Jc,this.package=B2,this.version=V2,this.config=Nc(b6,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>W2(n,this.config),this.facemesh=B6,this.age=Qy,this.gender=n2,this.emotion=l2,this.body=this.config.body.modelType.startsWith("posenet")?N2:z2,this.hand=M2}profile(){return this.config.profile?Zy:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Se(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?jv(t,n):0}async load(t=null){this.state="load";let n=ut();t&&(this.config=Nc(this.config,t)),this.firstRun&&(this.config.debug&&Se(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Se("configuration:",this.config),this.config.debug&&Se("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Yy:B6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?e2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?i2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?h2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?p2(this.config):null),this.models.handpose||(this.config.hand.enabled?D2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?T2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?P2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await e2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await i2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await h2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await p2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await D2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await T2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await P2(this.config))),this.firstRun&&(this.config.debug&&Se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(ut()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ut();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Se("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Se("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Se("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Rv();try{await this.tf.setBackend(this.config.backend)}catch(r){Se("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Se("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Se(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ut()-n)}}async detectFace(t){var u,c,h,d,p,m;let n,r,a,s,i,o=[];this.state="run:face",n=ut();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));this.perf.face=Math.trunc(ut()-n);for(let f of l){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Se("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?t2(f.image,this.config):{}:(this.state="run:age",n=ut(),r=this.config.face.age.enabled?await t2(f.image,this.config):{},this.perf.age=Math.trunc(ut()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?o2(f.image,this.config):{}:(this.state="run:gender",n=ut(),a=this.config.face.gender.enabled?await o2(f.image,this.config):{},this.perf.gender=Math.trunc(ut()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?d2(f.image,this.config):{}:(this.state="run:emotion",n=ut(),s=this.config.face.emotion.enabled?await d2(f.image,this.config):{},this.perf.emotion=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?f2(f.image,this.config):[]:(this.state="run:embedding",n=ut(),i=this.config.face.embedding.enabled?await f2(f.image,this.config):[],this.perf.embedding=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((c=f==null?void 0:f.annotations)==null?void 0:c.leftEyeIris)&&((h=f==null?void 0:f.annotations)==null?void 0:h.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let A=((d=f.annotations)==null?void 0:d.leftEyeIris)&&((p=f.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;o.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(m=f.image)==null||m.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async detect(t,n={}){return new Promise(async r=>{var d,p,m,f;this.state="config";let a;this.config=Nc(this.config,n),this.state="check";let s=this.sanity(t);s&&(Se(s,t),r({error:s}));let i,o,l,u=ut();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=ut();let c=W2(t,this.config);if(!c||!c.tensor){Se("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ut()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(c.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ut(),l=this.config.face.enabled?await this.detectFace(c.tensor):[],this.perf.face=Math.trunc(ut()-a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(c.tensor,this.config):[]:i=this.config.body.enabled?L2(c.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ut(),this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(c.tensor,this.config)):[]:i=this.config.body.enabled?await L2(c.tensor,this.config):[],this.perf.body=Math.trunc(ut()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(m=this.models.handpose)==null?void 0:m.estimateHands(c.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ut(),o=this.config.hand.enabled?await((f=this.models.handpose)==null?void 0:f.estimateHands(c.tensor,this.config)):[],this.perf.hand=Math.trunc(ut()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),c.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ut(),h=[...g6(l),...y6(i),...w6(o),...x6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ut()-a)),this.perf.total=Math.trunc(ut()-u),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:c.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(r0);break;case"full":n=await t(a0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+r0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+a0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(r0):t(a0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s}async warmup(t){let n=ut();t&&(this.config=Nc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=ut();return this.config.debug&&Se("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};export{V6 as default}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index 9ff131d1..5b3ee4e3 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/log.ts", "../node_modules/node-fetch/browser.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/seedrandom.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/index.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/tychei.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/seedrandom.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/index.js", "(disabled):path", "(disabled):worker_threads", "(disabled):perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "../node_modules/string_decoder/lib/string_decoder.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_to.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ceil.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clip_by_value.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cumsum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dense_bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depth_to_space.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depthwise_conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/diag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dilation2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div_no_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/elu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/erf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/exp.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expand_dims.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expm1.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/eye.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/fill.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floor.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/imag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_finite.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_inf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/leaky_relu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/linspace.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/local_response_normalization.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log1p.ts", "../node_modules/@tensorflow/tfjs-core/src/gradients.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/neg.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/softplus.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log_sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sub.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log_softmax.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/axis_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log_sum_exp.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/logical_and.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/logical_not.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/logical_or.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/logical_xor.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/max_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/max_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/max_pool_with_argmax.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/maximum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mean.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/minimum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mirror_pad.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mod.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/square.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/moments.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/multi_rnn_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/multinomial.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/not_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/zeros.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ones.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ones_like.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/outer_product.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pad.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pad1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pad2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pad3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pad4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/space_to_batch_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/pow.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/prelu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/prod.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/rand.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/rand_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/random_gamma.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/random_normal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/random_uniform.ts", 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"../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/human.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/imagefx.js", "../src/image.ts", "../config.js", "../src/sample.ts"], - "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 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xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var 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ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var 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\"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 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y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"../node_modules/@tensorflow/tfjs-data/src/datasets/text_line_dataset.ts", "../node_modules/@tensorflow/tfjs-data/src/datasets/csv_dataset.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/microphone_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/webcam_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/datasource.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/string_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/byte_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/file_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/url_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/util/source_util.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/file_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/human.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/gesture/gesture.ts", "../src/imagefx.js", "../src/image.ts", "../config.js", "../src/sample.ts"], + "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var 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asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModule) {\n WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n 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x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.esm.json b/dist/human.esm.json index 804d2d14..79b241c7 100644 --- a/dist/human.esm.json +++ b/dist/human.esm.json @@ -192,7 +192,7 @@ ] }, "src/posenet/modelBase.ts": { - "bytes": 1333, + "bytes": 1349, "imports": [ { "path": "dist/tfjs.esm.js", @@ -214,7 +214,7 @@ ] }, "src/posenet/keypoints.ts": { - "bytes": 2025, + "bytes": 2041, "imports": [] }, "src/posenet/vectors.ts": { @@ -240,7 +240,7 @@ ] }, "src/posenet/decodePose.ts": { - "bytes": 5152, + "bytes": 5182, "imports": [ { "path": "src/posenet/keypoints.ts", @@ -257,7 +257,7 @@ ] }, "src/posenet/decodeMultiple.ts": { - "bytes": 2301, + "bytes": 2323, "imports": [ { "path": "src/posenet/buildParts.ts", @@ -383,6 +383,23 @@ } ] }, + "src/blazepose/blazepose.ts": { + "bytes": 3327, + "imports": [ + { + "path": "src/log.ts", + "kind": "import-statement" + }, + { + "path": "dist/tfjs.esm.js", + "kind": "import-statement" + }, + { + "path": "src/profile.ts", + "kind": "import-statement" + } + ] + }, "src/gesture/gesture.ts": { "bytes": 4496, "imports": [] @@ -409,7 +426,7 @@ ] }, "config.js": { - "bytes": 10438, + "bytes": 10403, "imports": [] }, "src/sample.ts": { @@ -417,11 +434,11 @@ "imports": [] }, "package.json": { - "bytes": 2560, + "bytes": 2594, "imports": [] }, "src/human.ts": { - "bytes": 20100, + "bytes": 21042, "imports": [ { "path": "src/log.ts", @@ -467,6 +484,10 @@ "path": "src/handpose/handpose.ts", "kind": "import-statement" }, + { + "path": "src/blazepose/blazepose.ts", + "kind": "import-statement" + }, { "path": "src/gesture/gesture.ts", "kind": "import-statement" @@ -499,7 +520,7 @@ "imports": [], "exports": [], "inputs": {}, - "bytes": 1941875 + "bytes": 1948302 }, "dist/human.esm.js": { "imports": [], @@ -518,7 +539,7 @@ "bytesInOutput": 252 }, "dist/tfjs.esm.js": { - "bytesInOutput": 1056626 + "bytesInOutput": 1056636 }, "src/tfjs/backend.ts": { "bytesInOutput": 1053 @@ -539,7 +560,7 @@ "bytesInOutput": 5054 }, "src/human.ts": { - "bytesInOutput": 10612 + "bytesInOutput": 11312 }, "src/faceboxes/faceboxes.ts": { "bytesInOutput": 1576 @@ -563,7 +584,7 @@ "bytesInOutput": 1016 }, "src/posenet/modelBase.ts": { - "bytesInOutput": 646 + "bytesInOutput": 662 }, "src/posenet/heapSort.ts": { "bytesInOutput": 1017 @@ -572,7 +593,7 @@ "bytesInOutput": 456 }, "src/posenet/decodePose.ts": { - "bytesInOutput": 1292 + "bytesInOutput": 1283 }, "src/posenet/vectors.ts": { "bytesInOutput": 346 @@ -581,7 +602,7 @@ "bytesInOutput": 768 }, "src/posenet/decodeMultiple.ts": { - "bytesInOutput": 557 + "bytesInOutput": 529 }, "src/posenet/util.ts": { "bytesInOutput": 354 @@ -604,6 +625,9 @@ "src/handpose/anchors.ts": { "bytesInOutput": 126985 }, + "src/blazepose/blazepose.ts": { + "bytesInOutput": 1613 + }, "src/gesture/gesture.ts": { "bytesInOutput": 2391 }, @@ -614,16 +638,16 @@ "bytesInOutput": 2355 }, "config.js": { - "bytesInOutput": 1453 + "bytesInOutput": 1439 }, "src/sample.ts": { "bytesInOutput": 55295 }, "package.json": { - "bytesInOutput": 2572 + "bytesInOutput": 2596 } }, - "bytes": 1339605 + "bytes": 1341917 } } } diff --git a/dist/human.iife.json b/dist/human.iife.json index dde71fd0..31d0f9ce 100644 --- a/dist/human.iife.json +++ b/dist/human.iife.json @@ -192,7 +192,7 @@ ] }, "src/posenet/modelBase.ts": { - "bytes": 1333, + "bytes": 1349, "imports": [ { "path": "dist/tfjs.esm.js", @@ -214,7 +214,7 @@ ] }, "src/posenet/keypoints.ts": { - "bytes": 2025, + "bytes": 2041, "imports": [] }, "src/posenet/vectors.ts": { @@ -240,7 +240,7 @@ ] }, "src/posenet/decodePose.ts": { - "bytes": 5152, + "bytes": 5182, "imports": [ { "path": "src/posenet/keypoints.ts", @@ -257,7 +257,7 @@ ] }, "src/posenet/decodeMultiple.ts": { - "bytes": 2301, + "bytes": 2323, "imports": [ { "path": "src/posenet/buildParts.ts", @@ -383,6 +383,23 @@ } ] }, + "src/blazepose/blazepose.ts": { + "bytes": 3327, + "imports": [ + { + "path": "src/log.ts", + "kind": "import-statement" + }, + { + "path": "dist/tfjs.esm.js", + "kind": "import-statement" + }, + { + "path": "src/profile.ts", + "kind": "import-statement" + } + ] + }, "src/gesture/gesture.ts": { "bytes": 4496, "imports": [] @@ -409,7 +426,7 @@ ] }, "config.js": { - "bytes": 10438, + "bytes": 10403, "imports": [] }, "src/sample.ts": { @@ -417,11 +434,11 @@ "imports": [] }, "package.json": { - "bytes": 2560, + "bytes": 2594, "imports": [] }, "src/human.ts": { - "bytes": 20100, + "bytes": 21042, "imports": [ { "path": "src/log.ts", @@ -467,6 +484,10 @@ "path": "src/handpose/handpose.ts", "kind": "import-statement" }, + { + "path": "src/blazepose/blazepose.ts", + "kind": "import-statement" + }, { "path": "src/gesture/gesture.ts", "kind": "import-statement" @@ -499,7 +520,7 @@ "imports": [], "exports": [], "inputs": {}, - "bytes": 1941886 + "bytes": 1948313 }, "dist/human.ts": { "imports": [], @@ -513,13 +534,13 @@ "bytesInOutput": 1690 }, "src/human.ts": { - "bytesInOutput": 10648 + "bytesInOutput": 11348 }, "src/log.ts": { "bytesInOutput": 252 }, "dist/tfjs.esm.js": { - "bytesInOutput": 1056626 + "bytesInOutput": 1056636 }, "src/tfjs/backend.ts": { "bytesInOutput": 1053 @@ -561,7 +582,7 @@ "bytesInOutput": 1016 }, "src/posenet/modelBase.ts": { - "bytesInOutput": 646 + "bytesInOutput": 662 }, "src/posenet/heapSort.ts": { "bytesInOutput": 1017 @@ -570,7 +591,7 @@ "bytesInOutput": 456 }, "src/posenet/decodePose.ts": { - "bytesInOutput": 1292 + "bytesInOutput": 1283 }, "src/posenet/vectors.ts": { "bytesInOutput": 346 @@ -579,7 +600,7 @@ "bytesInOutput": 768 }, "src/posenet/decodeMultiple.ts": { - "bytesInOutput": 557 + "bytesInOutput": 529 }, "src/posenet/util.ts": { "bytesInOutput": 354 @@ -602,6 +623,9 @@ "src/handpose/anchors.ts": { "bytesInOutput": 126985 }, + "src/blazepose/blazepose.ts": { + "bytesInOutput": 1613 + }, "src/gesture/gesture.ts": { "bytesInOutput": 2391 }, @@ -612,16 +636,16 @@ "bytesInOutput": 2355 }, "config.js": { - "bytesInOutput": 1453 + "bytesInOutput": 1439 }, "src/sample.ts": { "bytesInOutput": 55295 }, "package.json": { - "bytesInOutput": 2572 + "bytesInOutput": 2596 } }, - "bytes": 1339647 + "bytes": 1341959 } } } diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js index f2fa03b1..09d99398 100644 --- a/dist/human.node-gpu.js +++ b/dist/human.node-gpu.js @@ -5,14 +5,14 @@ author: ' */ -var ht=Object.create,ye=Object.defineProperty,xt=Object.getPrototypeOf,yt=Object.prototype.hasOwnProperty,st=Object.getOwnPropertyNames,ot=Object.getOwnPropertyDescriptor;var se=t=>ye(t,"__esModule",{value:!0});var Ee=(t,e)=>()=>(e||(e={exports:{}},t(e.exports,e)),e.exports),Y=(t,e)=>{for(var n in e)ye(t,n,{get:e[n],enumerable:!0})},it=(t,e,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let r of st(e))!yt.call(t,r)&&r!=="default"&&ye(t,r,{get:()=>e[r],enumerable:!(n=ot(e,r))||n.enumerable});return t},v=t=>t&&t.__esModule?t:it(se(ye(t!=null?ht(xt(t)):{},"default",{value:t,enumerable:!0})),t);var E=Ee(Me=>{var 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L1=class{constructor(e,n){this.blazeFaceModel=e,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=ut(n.face.detector.inputSize),this.anchors=p.tensor2d(this.anchorsData),this.inputSize=p.tensor1d([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[n,r,A]=p.tidy(()=>{let s=e.resizeBilinear([this.width,this.height]),a=p.sub(s.div(127.5),1),d=this.blazeFaceModel.predict(a),l;if(Array.isArray(d)){let Z=d.sort((w,m)=>w.size-m.size),V=p.concat([Z[0],Z[2]],2),F=p.concat([Z[1],Z[3]],2);l=p.concat([F,V],1).squeeze(0)}else l=d.squeeze();let y=bt(l,this.anchors,this.inputSize),j=p.slice(l,[0,0],[-1,1]),I=p.sigmoid(j).squeeze();return[l,y,I]}),c=await p.image.nonMaxSuppressionAsync(r,A,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),_=c.arraySync();c.dispose();let 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b1=class{constructor(e,n,r){this.model=e,this.anchors=r.map(A=>[A.x_center,A.y_center]),this.anchorsTensor=b.tensor2d(this.anchors),this.inputSizeTensor=b.tensor1d([n,n]),this.doubleInputSizeTensor=b.tensor1d([n*2,n*2])}normalizeBoxes(e){return b.tidy(()=>{let n=b.slice(e,[0,0],[-1,2]),r=b.slice(e,[0,2],[-1,2]),A=b.add(b.div(n,this.inputSizeTensor),this.anchorsTensor),c=b.div(r,this.doubleInputSizeTensor),_=b.mul(b.sub(A,c),this.inputSizeTensor),h=b.mul(b.add(A,c),this.inputSizeTensor);return b.concat2d([_,h],1)})}normalizeLandmarks(e,n){return b.tidy(()=>{let r=b.add(b.div(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return b.mul(r,this.inputSizeTensor)})}async getBoxes(e,n){let r=this.model.predict(e),A=r.squeeze();r.dispose();let c=b.tidy(()=>b.sigmoid(b.slice(A,[0,0],[-1,1])).squeeze()),_=c.dataSync(),h=b.slice(A,[0,1],[-1,4]),x=this.normalizeBoxes(h);h.dispose();let o=await 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n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return A5(n)}var b0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function G(t,e){let n=0;for(let r=0;rT1([...c,1],n)),A=this.calculateLandmarksBoundingBox(r);return Te(ve(A),_5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),r=Te(ve(n),v0);r.palmLandmarks=[];for(let A=0;A[_[0]*(d[0]-this.inputSize/2),_[1]*(d[1]-this.inputSize/2),_[2]*d[2]]),x=g1(r,[0,0]),o=h.map(d=>[...T1(d,x),d[2]]),i=T0(A),s=[...he(n),1],a=[G(s,i[0]),G(s,i[1])];return o.map(d=>[d[0]+a[0],d[1]+a[1],d[2]])}async estimateHands(e,n){let r=!1,A;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(A=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,A&&A.length>0&&(A.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...A],this.storedBoxes.length>0&&(r=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=Pe.reshape(I,[-1,3]),F=V.arraySync();I.dispose(),V.dispose();let f=this.transformRawCoords(F,d,x,a),w=this.getBoxForHandLandmarks(f);this.storedBoxes[_]=w;let m={landmarks:f,confidence:Z,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};c.push(m)}else this.storedBoxes[_]=null;I.dispose()}else{let x=Te(ve(h),v0),o={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(o)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),r=e.map(_=>_[1]),A=[Math.min(...n),Math.min(...r)],c=[Math.max(...n),Math.max(...r)];return{startPoint:A,endPoint:c}}};var 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b1(e,t.hand.inputSize,z0),A=new v1(r,n,t.hand.inputSize),c=new E1(A);return t.hand.enabled&&t.debug&&u(`load model: ${t.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),t.hand.landmarks&&t.debug&&u(`load model: ${t.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),c}var R0=t=>{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),A=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&r&&A&&r.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},E0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let r=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(r)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let h=t[n].mesh[152][2];Math.abs(h)>10&&e.push({face:n,gesture:`head ${h<0?"up":"down"}`})}return e},M0=t=>{if(!t)return[];let e=[];for(let n=0;n{if(!t)return[];let e=[];for(let n=0;n0){let A=r.reduce((_,h)=>_.position[2]_.position[1](o[a]=0,s))},A=function(h,x){let o=t.createShader(x);if(t.shaderSource(o,h),t.compileShader(o),!t.getShaderParameter(o,t.COMPILE_STATUS))throw new Error("Filter: GL compile failed",t.getShaderInfoLog(o));return o};this.uniform={},this.attribute={};let 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B1=class{constructor(e,n){this.blazeFaceModel=e,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=Pt(n.face.detector.inputSize),this.anchors=u.tensor2d(this.anchorsData),this.inputSize=u.tensor1d([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[n,A,r]=u.tidy(()=>{let y=e.resizeBilinear([this.width,this.height]),a=u.sub(y.div(127.5),1),d=this.blazeFaceModel.predict(a),l;if(Array.isArray(d)){let Z=d.sort((w,m)=>w.size-m.size),V=u.concat([Z[0],Z[2]],2),Y=u.concat([Z[1],Z[3]],2);l=u.concat([Y,V],1).squeeze(0)}else l=d.squeeze();let s=Rt(l,this.anchors,this.inputSize),j=u.slice(l,[0,0],[-1,1]),I=u.sigmoid(j).squeeze();return[l,s,I]}),c=await u.image.nonMaxSuppressionAsync(A,r,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),_=c.arraySync();c.dispose();let x=_.map(y=>u.slice(A,[y,0],[1,-1])).map(y=>{let a=y.arraySync();return y.dispose(),a}),o=r.dataSync(),i=[];for(let y=0;ythis.config.face.detector.minConfidence){let l=zt(x[y]),s=this.anchorsData[a],j=u.tidy(()=>u.slice(n,[a,J1-1],[1,-1]).squeeze().reshape([J1,-1]));i.push({box:l,landmarks:j,anchor:s,confidence:d})}}return n.dispose(),A.dispose(),r.dispose(),n.dispose(),{boxes:i,scaleFactor:[e.shape[2]/this.width,e.shape[1]/this.height]}}};async function K1(t){let e=await u.loadGraphModel(t.face.detector.modelPath,{fromTFHub:t.face.detector.modelPath.includes("tfhub.dev")}),n=new B1(e,t);return t.debug&&p(`load model: ${t.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}var H=v(R());var U1=v(R());function C1(t,e){let n=[t.startPoint[0]*e[0],t.startPoint[1]*e[1]],A=[t.endPoint[0]*e[0],t.endPoint[1]*e[1]];return{startPoint:n,endPoint:A}}function _e(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function 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x,o=0,i;if(n.face.detector.rotation){let[f,w]=_.landmarks.length>=jt?It:kt;o=D1(_.landmarks[f],_.landmarks[w]);let m=he({startPoint:_.startPoint,endPoint:_.endPoint}),g=[m[0]/e.shape[2],m[1]/e.shape[1]],P=H.image.rotateWithOffset(e,o,0,g);i=Oe(-o,m),x=je({startPoint:_.startPoint,endPoint:_.endPoint},P,[this.meshHeight,this.meshWidth]).div(255)}else{i=de;let f=e.clone();x=je({startPoint:_.startPoint,endPoint:_.endPoint},f,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,confidence:_.confidence,image:x};let[,y,a]=this.meshDetector.predict(x),d=y.dataSync()[0];if(d_!==null),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),A=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:r,endPoint:c,landmarks:e}}};var ct=v(r0());var Je={};J(Je,{FaceBoxes:()=>Be,load:()=>qt});var te=v(R());var Ye={};function F(t,e){if(!e||!e.kernels)return;let 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ne,Ge={gender:""},pe=Number.MAX_SAFE_INTEGER,Qe=!1,$e=[.2989,.587,.114];async function e1(t){return ne||(ne=await M.loadGraphModel(t.face.gender.modelPath),Qe=ne.inputs[0].shape[3]===1,t.debug&&p(`load model: ${t.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),ne}async function t1(t,e){return ne?pe{let A=M.image.resizeBilinear(t,[e.face.gender.inputSize,e.face.gender.inputSize],!1),r;Qe?r=M.tidy(()=>{let[h,x,o]=M.split(A,3,3),i=M.mul(h,$e[0]),y=M.mul(x,$e[1]),a=M.mul(o,$e[2]);return M.addN([i,y,a]).sub(.5).mul(2)}):r=M.mul(A,[255]),M.dispose(A);let c,_={gender:"",confidence:0};if(!e.profile)e.face.gender.enabled&&(c=await ne.predict(r));else{let h=e.face.gender.enabled?await M.profile(()=>ne.predict(r)):{};c=h.result.clone(),h.result.dispose(),F("gender",h)}if(r.dispose(),c){let h=c.dataSync();if(Qe){let x=Math.trunc(100*Math.abs(h[0]-h[1]))/100;x>e.face.gender.minConfidence&&(_.gender=h[0]>h[1]?"female":"male",_.confidence=x)}else{let 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d;if(e.profile){let l=await W.profile(()=>Ae.predict(y));d=l.result.dataSync(),l.result.dispose(),F("emotion",l)}else{let l=await Ae.predict(y);d=l.dataSync(),W.dispose(l)}for(let l=0;le.face.emotion.minConfidence&&a.push({score:Math.min(.99,Math.trunc(100*A0*d[l])/100),emotion:Yt[l]});a.sort((l,s)=>s.score-l.score)}y.dispose(),r1=a,n(a)})):null}var G=v(R());var ce;async function h1(t){return ce||(ce=await G.loadGraphModel(t.face.embedding.modelPath),t.debug&&p(`load model: ${t.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),ce}function c0(t,e){if(!t||!e||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)!==(e==null?void 0:e.length))return 0;let n=2,A=10*t.map((r,c)=>r-e[c]).reduce((r,c)=>r+c**n,0)**(1/n);return Math.trunc(1e3*(1-A))/1e3}async function x1(t,e){return ce?new Promise(async n=>{let A=G.image.resizeBilinear(t,[e.face.embedding.inputSize,e.face.embedding.inputSize],!1),r=[];if(e.face.embedding.enabled)if(e.profile){let c=await 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o1=class{constructor(e,n){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=n}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(y1(e),e);)this.exchange(e,y1(e)),e=y1(e)}sink(e){for(;2*e<=this.numberOfElements;){let n=2*e;if(ne){x=!1;break}if(!x)break}return x}function h0(t,e,n){let[A,r,c]=n.shape,_=new o1(A*r*c,({score:h})=>h);for(let h=0;hn?n:t}function s0(t,e,n,A){let r=n-t,c=A-e;return r*r+c*c}function l1(t,e){return{x:t.x+e.x,y:t.y+e.y}}var L=v(R()),Te=v(be());function y0(t,e){let n=e.shape[0],A=new Float32Array(n);for(let 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r.dispose(),o.dispose(),{keypoints:d,score:A/a.length}}var _5=1,m0=16;function p0(t,e,{x:n,y:A},r){return t.some(({keypoints:c})=>{let _=c[r].position;return s0(A,n,_.y,_.x)<=e})}function h5(t,e,n){return n.reduce((r,{position:c,score:_},h)=>(p0(t,e,c,h)||(r+=_),r),0)/n.length}function u0(t,e,n,A,r){let c=[],_=h0(r.body.scoreThreshold,_5,t),h=r.body.nmsRadius^2;for(;c.lengthr.body.scoreThreshold&&c.push({keypoints:i,score:y})}return c}async function b0(t){return Promise.all(t.map(e=>e.buffer()))}function x5(t,e,n){return{score:t.score,keypoints:t.keypoints.map(({score:A,part:r,position:c})=>({score:A,part:r,position:{x:c.x*n,y:c.y*e}}))}}function g0(t,[e,n]){let A=t.squeeze(0),r=A.resizeBilinear([e,n]);return A.dispose(),r}function p1(t,[e,n],[A,r]){return t.map(_=>x5(_,e/A,n/r))}async function s5(t,e,n){return new Promise(async A=>{let r=t.shape[1],c=t.shape[2],_=await b0([e.heatmapScores,e.offsets,e.displacementFwd,e.displacementBwd]),h=_[0],x=_[1],o=_[2],i=_[3],y=await u0(h,x,o,i,n),a=p1(y,[r,c],[n.body.inputSize,n.body.inputSize]);A(a)})}async function y5(t,e,n){return new Promise(async A=>{let r=t.shape[1],c=t.shape[2],h=[await f0(e.heatmapScores,e.offsets,n)],x=p1(h,[r,c],[n.body.inputSize,n.body.inputSize]);A(x)})}var b1=class{constructor(e){this.baseModel=e}async estimatePoses(e,n){let A=g0(e,[n.body.inputSize,n.body.inputSize]),r=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await y5(e,r,n):await s5(e,r,n);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function g1(t){let e=await T0.loadGraphModel(t.body.modelPath),n=new s1(e);return t.debug&&p(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`),new b1(n)}var R1={};J(R1,{HandPose:()=>S1,load:()=>W1});var E1=v(R());var b=v(R());var v0=v(R());function ve(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function se(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function P0(t,e,n){let A=e.shape[1],r=e.shape[2],c=[[t.startPoint[1]/A,t.startPoint[0]/r,t.endPoint[1]/A,t.endPoint[0]/r]];return v0.image.cropAndResize(e,c,[0],n)}function z0(t,e){let n=[t.startPoint[0]*e[0],t.startPoint[1]*e[1]],A=[t.endPoint[0]*e[0],t.endPoint[1]*e[1]],r=t.palmLandmarks.map(c=>[c[0]*e[0],c[1]*e[1]]);return{startPoint:n,endPoint:A,palmLandmarks:r,confidence:t.confidence}}function Pe(t,e=1.5){let n=se(t),A=ve(t),r=[e*A[0]/2,e*A[1]/2],c=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:c,endPoint:_,palmLandmarks:t.palmLandmarks}}function ze(t){let e=se(t),n=ve(t),r=Math.max(...n)/2,c=[e[0]-r,e[1]-r],_=[e[0]+r,e[1]+r];return{startPoint:c,endPoint:_,palmLandmarks:t.palmLandmarks}}var T1=class{constructor(e,n,A){this.model=e,this.anchors=A.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=b.tensor2d(this.anchors),this.inputSizeTensor=b.tensor1d([n,n]),this.doubleInputSizeTensor=b.tensor1d([n*2,n*2])}normalizeBoxes(e){return b.tidy(()=>{let n=b.slice(e,[0,0],[-1,2]),A=b.slice(e,[0,2],[-1,2]),r=b.add(b.div(n,this.inputSizeTensor),this.anchorsTensor),c=b.div(A,this.doubleInputSizeTensor),_=b.mul(b.sub(r,c),this.inputSizeTensor),h=b.mul(b.add(r,c),this.inputSizeTensor);return b.concat2d([_,h],1)})}normalizeLandmarks(e,n){return b.tidy(()=>{let A=b.add(b.div(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return b.mul(A,this.inputSizeTensor)})}async getBoxes(e,n){let A=this.model.predict(e),r=A.squeeze();A.dispose();let c=b.tidy(()=>b.sigmoid(b.slice(r,[0,0],[-1,1])).squeeze()),_=c.dataSync(),h=b.slice(r,[0,1],[-1,4]),x=this.normalizeBoxes(h);h.dispose();let o=await b.image.nonMaxSuppressionAsync(x,_,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),i=o.arraySync();c.dispose(),o.dispose();let y=[];for(let a of i)if(_[a]>=n.hand.minConfidence){let d=b.slice(x,[a,0],[1,-1]),l=b.slice(r,[a,5],[1,14]),s=b.tidy(()=>this.normalizeLandmarks(l,a).reshape([-1,2]));l.dispose(),y.push({box:d,palmLandmarks:s,confidence:_[a]})}return r.dispose(),x.dispose(),y}async estimateHandBounds(e,n){let A=e.shape[1],r=e.shape[2],c=b.tidy(()=>e.resizeBilinear([n.hand.inputSize,n.hand.inputSize]).div(127.5).sub(1)),_=await this.getBoxes(c,n);c.dispose();let h=[];if(!_||_.length===0)return h;for(let x of _){let o=x.box.dataSync(),i=o.slice(0,2),y=o.slice(2,4),a=x.palmLandmarks.arraySync();x.box.dispose(),x.palmLandmarks.dispose(),h.push(z0({startPoint:i,endPoint:y,palmLandmarks:a,confidence:x.confidence},[r/n.hand.inputSize,A/n.hand.inputSize]))}return h}};var Re=v(R());function o5(t){return t-2*Math.PI*Math.floor((t+Math.PI)/(2*Math.PI))}function R0(t,e){let n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return o5(n)}var E0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function Q(t,e){let n=0;for(let A=0;AP1([...c,1],n)),r=this.calculateLandmarksBoundingBox(A);return Pe(ze(r),a5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),A=Pe(ze(n),W0);A.palmLandmarks=[];for(let r=0;r[_[0]*(d[0]-this.inputSize/2),_[1]*(d[1]-this.inputSize/2),_[2]*d[2]]),x=v1(A,[0,0]),o=h.map(d=>[...P1(d,x),d[2]]),i=S0(r),y=[...se(n),1],a=[Q(y,i[0]),Q(y,i[1])];return o.map(d=>[d[0]+a[0],d[1]+a[1],d[2]])}async estimateHands(e,n){let A=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(A=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=Re.reshape(I,[-1,3]),Y=V.arraySync();I.dispose(),V.dispose();let f=this.transformRawCoords(Y,d,x,a),w=this.getBoxForHandLandmarks(f);this.storedBoxes[_]=w;let m={landmarks:f,confidence:Z,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};c.push(m)}else this.storedBoxes[_]=null;I.dispose()}else{let x=Pe(ze(h),W0),o={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(o)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),A=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:r,endPoint:c}}};var 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$.loadGraphModel(t.body.modelPath),q.width=parseInt(q.signature.inputs["input_1:0"].tensorShape.dim[2].size),q.height=parseInt(q.signature.inputs["input_1:0"].tensorShape.dim[1].size),t.debug&&p(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`)),q}async function O1(t,e){if(!q||!e.body.enabled)return null;let n={width:t.shape[2],height:t.shape[1]},A=$.image.resizeBilinear(t,[q.width||e.body.inputSize,q.height||e.body.inputSize],!1),r=$.div(A,[255]);A.dispose();let c;if(e.profile){let h=await $.profile(()=>q.predict(r));c=h.result.find(x=>x.size===195).dataSync(),h.result.forEach(x=>x.dispose()),F("blazepose",h)}else{let h=await q.predict(r);c=h.find(x=>x.size===195||x.size===155).dataSync(),h.forEach(x=>x.dispose())}r.dispose();let _=[];for(let h=0;h{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),r=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&A&&r&&A.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},I0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let A=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(A)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${A<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let 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null}simmilarity(e,n){return this.config.face.embedding.enabled?$1(e,n):0}async load(e=null){this.state="load";let n=T();e&&(this.config=xe(this.config,e)),this.firstRun&&(this.config.debug&&u(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?qe:$0;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Be(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Qe(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?r1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?c1(this.config):null),this.models.posenet||(this.config.body.enabled?p1(this.config):null),this.models.handpose||(this.config.hand.enabled?M1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Be(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await 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backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&u("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||u("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&k1();try{await this.tf.setBackend(this.config.backend)}catch(r){u("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(T()-n)}}async detectFace(e){var o,i,s,a,d,l;let n,r,A,c,_,h=[];this.state="run:face",n=T();let x=await((o=this.models.face)==null?void 0:o.estimateFaces(e,this.config));this.perf.face=Math.trunc(T()-n);for(let y of x){if(this.analyze("Get Face"),!y.image||y.image.isDisposedInternal){u("Face object is disposed:",y.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?Ke(y.image,this.config):{}:(this.state="run:age",n=T(),r=this.config.face.age.enabled?await Ke(y.image,this.config):{},this.perf.age=Math.trunc(T()-n)),this.analyze("Start Gender:"),this.config.async?A=this.config.face.gender.enabled?$e(y.image,this.config):{}:(this.state="run:gender",n=T(),A=this.config.face.gender.enabled?await $e(y.image,this.config):{},this.perf.gender=Math.trunc(T()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?A1(y.image,this.config):{}:(this.state="run:emotion",n=T(),c=this.config.face.emotion.enabled?await A1(y.image,this.config):{},this.perf.emotion=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?_1(y.image,this.config):[]:(this.state="run:embedding",n=T(),_=this.config.face.embedding.enabled?await _1(y.image,this.config):[],this.perf.embedding=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.config.async&&([r,A,c,_]=await Promise.all([r,A,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((i=y==null?void 0:y.annotations)==null?void 0:i.leftEyeIris)&&((s=y==null?void 0:y.annotations)==null?void 0:s.rightEyeIris)&&(delete y.annotations.leftEyeIris,delete y.annotations.rightEyeIris);let j=((a=y.annotations)==null?void 0:a.leftEyeIris)&&((d=y.annotations)==null?void 0:d.rightEyeIris)?11.7*Math.max(Math.abs(y.annotations.leftEyeIris[3][0]-y.annotations.leftEyeIris[1][0]),Math.abs(y.annotations.rightEyeIris[4][1]-y.annotations.rightEyeIris[2][1])):0;h.push({confidence:y.confidence,faceConfidence:y.faceConfidence,boxConfidence:y.boxConfidence,box:y.box,mesh:y.mesh,boxRaw:y.boxRaw,meshRaw:y.meshRaw,annotations:y.annotations,age:r.age,gender:A.gender,genderConfidence:A.confidence,emotion:c,embedding:_,iris:j!==0?Math.trunc(j)/100:0}),(l=y.image)==null||l.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async r=>{var a,d,l,y;this.state="config";let A;this.config=xe(this.config,n),this.state="check";let c=this.sanity(e);c&&(u(c,e),r({error:c}));let _,h,x,o=T();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),A=T();let i=W0(e,this.config);if(!i||!i.tensor){u("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(T()-A),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(i.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",A=T(),x=this.config.face.enabled?await this.detectFace(i.tensor):[],this.perf.face=Math.trunc(T()-A)),this.analyze("Start Body:"),this.config.async?(_=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(i.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",A=T(),_=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(i.tensor,this.config)):[],this.perf.body=Math.trunc(T()-A)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(l=this.models.handpose)==null?void 0:l.estimateHands(i.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",A=T(),h=this.config.hand.enabled?await((y=this.models.handpose)==null?void 0:y.estimateHands(i.tensor,this.config)):[],this.perf.hand=Math.trunc(T()-A)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),i.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let s=[];this.config.gesture.enabled&&(A=T(),s=[...E0(x),...R0(_),...S0(h),...M0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(T()-A)),this.perf.total=Math.trunc(T()-o),this.state="idle",r({face:x,body:_,hand:h,gesture:s,performance:this.perf,canvas:i.canvas})})}async warmupBitmap(){let e=(A,c="application/octet-stream")=>fetch(`data:${c};base64,${A}`).then(_=>_.blob()),n,r;switch(this.config.warmup){case"face":n=await e(ze);break;case"full":n=await e(Re);break;default:n=null}if(n){let A=await createImageBitmap(n);r=await this.detect(A,this.config),A.close()}return r}async warmupCanvas(){return new Promise(e=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+ze;break;case"full":r=1200,n="data:image/jpeg;base64,"+Re;break;default:n=null}let A=new Image(r,r);A.onload=()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");c.width=r,c.height=r;let _=c.getContext("2d");_==null||_.drawImage(A,0,0);let h=_==null?void 0:_.getImageData(0,0,r,r);this.detect(h,this.config).then(x=>e(x))},n?A.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(ze):e(Re),r=Q0.node.decodeJpeg(n),A=r.expandDims(0);this.tf.dispose(r);let c=await this.detect(A,this.config);return this.tf.dispose(A),c}async warmup(e){let n=T();e&&(this.config=xe(this.config,e));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let A;typeof createImageBitmap=="function"?A=await this.warmupBitmap():typeof Image!="undefined"?A=await this.warmupCanvas():A=await this.warmupNode(),this.config.videoOptimized=r;let c=T();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(c-n),"ms",A),A}}; +2Q==`;var Z1={};J(Z1,{author:()=>K0,browser:()=>J0,bugs:()=>U0,default:()=>m5,dependencies:()=>$0,description:()=>H0,devDependencies:()=>tt,engines:()=>G0,homepage:()=>C0,keywords:()=>rt,license:()=>D0,main:()=>q0,module:()=>Y0,name:()=>L0,peerDependencies:()=>et,repository:()=>Q0,scripts:()=>nt,sideEffects:()=>F0,types:()=>B0,version:()=>V1});var L0="@vladmandic/human",V1="0.40.0",H0="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture 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--trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",lint:"eslint src demo server",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",update:"npm update --depth 20 --force && npm dedupe && npm prune && npm audit"},rt=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],m5={name:L0,version:V1,description:H0,sideEffects:F0,main:q0,module:Y0,browser:J0,types:B0,author:K0,bugs:U0,homepage:C0,license:D0,engines:G0,repository:Q0,dependencies:$0,peerDependencies:et,devDependencies:tt,scripts:nt,keywords:rt};var T=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function ye(...t){let e=n=>n&&typeof n=="object";return t.reduce((n,A)=>(Object.keys(A||{}).forEach(r=>{let c=n[r],_=A[r];Array.isArray(c)&&Array.isArray(_)?n[r]=c.concat(..._):e(c)&&e(_)?n[r]=ye(c,_):n[r]=_}),n),{})}var _t=class{constructor(e={}){this.tf=At,this.package=Z1,this.version=V1,this.config=ye(X0,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>I1(n,this.config),this.facemesh=ct,this.age=Ke,this.gender=De,this.emotion=n1,this.body=this.config.body.modelType.startsWith("posenet")?u1:N1,this.hand=R1}profile(){return this.config.profile?Ye:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,A=this.numTensors;this.numTensors=n;let r=n-A;r!==0&&p(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(e,n){return this.config.face.embedding.enabled?c0(e,n):0}async load(e=null){this.state="load";let n=T();e&&(this.config=ye(this.config,e)),this.firstRun&&(this.config.debug&&p(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("tf flags:",this.tf.ENV.flags)));let A=this.config.face.detector.modelPath.includes("faceboxes")?Je:ct;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?A.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Ue(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?e1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?c1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?h1(this.config):null),this.models.handpose||(this.config.hand.enabled?W1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?g1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?j1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await A.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Ue(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await e1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await c1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await h1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await W1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await g1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await j1(this.config))),this.firstRun&&(this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(T()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let n=T();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&p("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&p("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||p("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Y1();try{await this.tf.setBackend(this.config.backend)}catch(A){p("error: cannot set backend:",this.config.backend,A)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(p("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let A=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&p(`gl version:${A.getParameter(A.VERSION)} renderer:${A.getParameter(A.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(T()-n)}}async detectFace(e){var o,i,y,a,d,l;let n,A,r,c,_,h=[];this.state="run:face",n=T();let x=await((o=this.models.face)==null?void 0:o.estimateFaces(e,this.config));this.perf.face=Math.trunc(T()-n);for(let s of x){if(this.analyze("Get Face"),!s.image||s.image.isDisposedInternal){p("Face object is disposed:",s.image);continue}this.analyze("Start Age:"),this.config.async?A=this.config.face.age.enabled?Ce(s.image,this.config):{}:(this.state="run:age",n=T(),A=this.config.face.age.enabled?await Ce(s.image,this.config):{},this.perf.age=Math.trunc(T()-n)),this.analyze("Start Gender:"),this.config.async?r=this.config.face.gender.enabled?t1(s.image,this.config):{}:(this.state="run:gender",n=T(),r=this.config.face.gender.enabled?await t1(s.image,this.config):{},this.perf.gender=Math.trunc(T()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?_1(s.image,this.config):{}:(this.state="run:emotion",n=T(),c=this.config.face.emotion.enabled?await _1(s.image,this.config):{},this.perf.emotion=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?x1(s.image,this.config):[]:(this.state="run:embedding",n=T(),_=this.config.face.embedding.enabled?await x1(s.image,this.config):[],this.perf.embedding=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.config.async&&([A,r,c,_]=await Promise.all([A,r,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((i=s==null?void 0:s.annotations)==null?void 0:i.leftEyeIris)&&((y=s==null?void 0:s.annotations)==null?void 0:y.rightEyeIris)&&(delete s.annotations.leftEyeIris,delete s.annotations.rightEyeIris);let j=((a=s.annotations)==null?void 0:a.leftEyeIris)&&((d=s.annotations)==null?void 0:d.rightEyeIris)?11.7*Math.max(Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0]),Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])):0;h.push({confidence:s.confidence,faceConfidence:s.faceConfidence,boxConfidence:s.boxConfidence,box:s.box,mesh:s.mesh,boxRaw:s.boxRaw,meshRaw:s.meshRaw,annotations:s.annotations,age:A.age,gender:r.gender,genderConfidence:r.confidence,emotion:c,embedding:_,iris:j!==0?Math.trunc(j)/100:0}),(l=s.image)==null||l.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async A=>{var a,d,l,s;this.state="config";let r;this.config=ye(this.config,n),this.state="check";let c=this.sanity(e);c&&(p(c,e),A({error:c}));let _,h,x,o=T();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=T();let i=I1(e,this.config);if(!i||!i.tensor){p("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(T()-r),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(i.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=T(),x=this.config.face.enabled?await this.detectFace(i.tensor):[],this.perf.face=Math.trunc(T()-r)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(i.tensor,this.config):[]:_=this.config.body.enabled?O1(i.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",r=T(),this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(i.tensor,this.config)):[]:_=this.config.body.enabled?await O1(i.tensor,this.config):[],this.perf.body=Math.trunc(T()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(l=this.models.handpose)==null?void 0:l.estimateHands(i.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=T(),h=this.config.hand.enabled?await((s=this.models.handpose)==null?void 0:s.estimateHands(i.tensor,this.config)):[],this.perf.hand=Math.trunc(T()-r)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),i.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let y=[];this.config.gesture.enabled&&(r=T(),y=[...I0(x),...O0(_),...V0(h),...Z0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(T()-r)),this.perf.total=Math.trunc(T()-o),this.state="idle",A({face:x,body:_,hand:h,gesture:y,performance:this.perf,canvas:i.canvas})})}async warmupBitmap(){let e=(r,c="application/octet-stream")=>fetch(`data:${c};base64,${r}`).then(_=>_.blob()),n,A;switch(this.config.warmup){case"face":n=await e(Ee);break;case"full":n=await e(Me);break;default:n=null}if(n){let r=await createImageBitmap(n);A=await this.detect(r,this.config),r.close()}return A}async warmupCanvas(){return new Promise(e=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+Ee;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+Me;break;default:n=null}let r=new Image;r.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=r.naturalWidth,c.height=r.naturalHeight;let _=c.getContext("2d");_==null||_.drawImage(r,0,0);let h=await this.detect(c,this.config);e(h)},n?r.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Ee):e(Me),A=At.node.decodeJpeg(n),r=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(r,this.config);return this.tf.dispose(r),c}async warmup(e){let n=T();e&&(this.config=ye(this.config,e));let A=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=A;let c=T();return this.config.debug&&p("Warmup",this.config.warmup,Math.round(c-n),"ms",r),r}}; //# sourceMappingURL=human.node-gpu.js.map diff --git a/dist/human.node-gpu.js.map b/dist/human.node-gpu.js.map index a553781a..077d70d4 100644 --- a/dist/human.node-gpu.js.map +++ b/dist/human.node-gpu.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/human.ts", "../src/log.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n 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0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n 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0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n 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0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/human.ts", "../src/log.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], + "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 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0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return A5(n)}var b0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function G(t,e){let n=0;for(let r=0;rT1([...c,1],n)),A=this.calculateLandmarksBoundingBox(r);return Te(ve(A),_5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),r=Te(ve(n),v0);r.palmLandmarks=[];for(let A=0;A[_[0]*(d[0]-this.inputSize/2),_[1]*(d[1]-this.inputSize/2),_[2]*d[2]]),x=g1(r,[0,0]),o=h.map(d=>[...T1(d,x),d[2]]),i=T0(A),s=[...he(n),1],a=[G(s,i[0]),G(s,i[1])];return o.map(d=>[d[0]+a[0],d[1]+a[1],d[2]])}async estimateHands(e,n){let r=!1,A;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(A=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,A&&A.length>0&&(A.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...A],this.storedBoxes.length>0&&(r=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=Pe.reshape(I,[-1,3]),F=V.arraySync();I.dispose(),V.dispose();let f=this.transformRawCoords(F,d,x,a),w=this.getBoxForHandLandmarks(f);this.storedBoxes[_]=w;let m={landmarks:f,confidence:Z,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};c.push(m)}else this.storedBoxes[_]=null;I.dispose()}else{let x=Te(ve(h),v0),o={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(o)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),r=e.map(_=>_[1]),A=[Math.min(...n),Math.min(...r)],c=[Math.max(...n),Math.max(...r)];return{startPoint:A,endPoint:c}}};var 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b1(e,t.hand.inputSize,z0),A=new v1(r,n,t.hand.inputSize),c=new E1(A);return t.hand.enabled&&t.debug&&u(`load model: ${t.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),t.hand.landmarks&&t.debug&&u(`load model: ${t.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),c}var R0=t=>{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),A=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&r&&A&&r.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},E0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let r=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(r)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let h=t[n].mesh[152][2];Math.abs(h)>10&&e.push({face:n,gesture:`head ${h<0?"up":"down"}`})}return e},M0=t=>{if(!t)return[];let e=[];for(let n=0;n{if(!t)return[];let e=[];for(let n=0;n0){let A=r.reduce((_,h)=>_.position[2]_.position[1](o[a]=0,s))},A=function(h,x){let o=t.createShader(x);if(t.shaderSource(o,h),t.compileShader(o),!t.getShaderParameter(o,t.COMPILE_STATUS))throw new Error("Filter: GL compile failed",t.getShaderInfoLog(o));return o};this.uniform={},this.attribute={};let 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B1=class{constructor(e,n){this.blazeFaceModel=e,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=Pt(n.face.detector.inputSize),this.anchors=u.tensor2d(this.anchorsData),this.inputSize=u.tensor1d([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(e){if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return null;let[n,A,r]=u.tidy(()=>{let y=e.resizeBilinear([this.width,this.height]),a=u.sub(y.div(127.5),1),d=this.blazeFaceModel.predict(a),l;if(Array.isArray(d)){let Z=d.sort((w,m)=>w.size-m.size),V=u.concat([Z[0],Z[2]],2),Y=u.concat([Z[1],Z[3]],2);l=u.concat([Y,V],1).squeeze(0)}else l=d.squeeze();let s=Rt(l,this.anchors,this.inputSize),j=u.slice(l,[0,0],[-1,1]),I=u.sigmoid(j).squeeze();return[l,s,I]}),c=await u.image.nonMaxSuppressionAsync(A,r,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),_=c.arraySync();c.dispose();let 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x,o=0,i;if(n.face.detector.rotation){let[f,w]=_.landmarks.length>=jt?It:kt;o=D1(_.landmarks[f],_.landmarks[w]);let m=he({startPoint:_.startPoint,endPoint:_.endPoint}),g=[m[0]/e.shape[2],m[1]/e.shape[1]],P=H.image.rotateWithOffset(e,o,0,g);i=Oe(-o,m),x=je({startPoint:_.startPoint,endPoint:_.endPoint},P,[this.meshHeight,this.meshWidth]).div(255)}else{i=de;let f=e.clone();x=je({startPoint:_.startPoint,endPoint:_.endPoint},f,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,confidence:_.confidence,image:x};let[,y,a]=this.meshDetector.predict(x),d=y.dataSync()[0];if(d_!==null),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),A=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:r,endPoint:c,landmarks:e}}};var ct=v(r0());var Je={};J(Je,{FaceBoxes:()=>Be,load:()=>qt});var te=v(R());var Ye={};function F(t,e){if(!e||!e.kernels)return;let 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ne,Ge={gender:""},pe=Number.MAX_SAFE_INTEGER,Qe=!1,$e=[.2989,.587,.114];async function e1(t){return ne||(ne=await M.loadGraphModel(t.face.gender.modelPath),Qe=ne.inputs[0].shape[3]===1,t.debug&&p(`load model: ${t.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),ne}async function t1(t,e){return ne?pe{let A=M.image.resizeBilinear(t,[e.face.gender.inputSize,e.face.gender.inputSize],!1),r;Qe?r=M.tidy(()=>{let[h,x,o]=M.split(A,3,3),i=M.mul(h,$e[0]),y=M.mul(x,$e[1]),a=M.mul(o,$e[2]);return M.addN([i,y,a]).sub(.5).mul(2)}):r=M.mul(A,[255]),M.dispose(A);let c,_={gender:"",confidence:0};if(!e.profile)e.face.gender.enabled&&(c=await ne.predict(r));else{let h=e.face.gender.enabled?await M.profile(()=>ne.predict(r)):{};c=h.result.clone(),h.result.dispose(),F("gender",h)}if(r.dispose(),c){let h=c.dataSync();if(Qe){let x=Math.trunc(100*Math.abs(h[0]-h[1]))/100;x>e.face.gender.minConfidence&&(_.gender=h[0]>h[1]?"female":"male",_.confidence=x)}else{let 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d;if(e.profile){let l=await W.profile(()=>Ae.predict(y));d=l.result.dataSync(),l.result.dispose(),F("emotion",l)}else{let l=await Ae.predict(y);d=l.dataSync(),W.dispose(l)}for(let l=0;le.face.emotion.minConfidence&&a.push({score:Math.min(.99,Math.trunc(100*A0*d[l])/100),emotion:Yt[l]});a.sort((l,s)=>s.score-l.score)}y.dispose(),r1=a,n(a)})):null}var G=v(R());var ce;async function h1(t){return ce||(ce=await G.loadGraphModel(t.face.embedding.modelPath),t.debug&&p(`load model: ${t.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),ce}function c0(t,e){if(!t||!e||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)!==(e==null?void 0:e.length))return 0;let n=2,A=10*t.map((r,c)=>r-e[c]).reduce((r,c)=>r+c**n,0)**(1/n);return Math.trunc(1e3*(1-A))/1e3}async function x1(t,e){return ce?new Promise(async n=>{let A=G.image.resizeBilinear(t,[e.face.embedding.inputSize,e.face.embedding.inputSize],!1),r=[];if(e.face.embedding.enabled)if(e.profile){let c=await 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o1=class{constructor(e,n){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=n}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(y1(e),e);)this.exchange(e,y1(e)),e=y1(e)}sink(e){for(;2*e<=this.numberOfElements;){let n=2*e;if(ne){x=!1;break}if(!x)break}return x}function h0(t,e,n){let[A,r,c]=n.shape,_=new o1(A*r*c,({score:h})=>h);for(let h=0;hn?n:t}function s0(t,e,n,A){let r=n-t,c=A-e;return r*r+c*c}function l1(t,e){return{x:t.x+e.x,y:t.y+e.y}}var L=v(R()),Te=v(be());function y0(t,e){let n=e.shape[0],A=new Float32Array(n);for(let 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r.dispose(),o.dispose(),{keypoints:d,score:A/a.length}}var _5=1,m0=16;function p0(t,e,{x:n,y:A},r){return t.some(({keypoints:c})=>{let _=c[r].position;return s0(A,n,_.y,_.x)<=e})}function h5(t,e,n){return n.reduce((r,{position:c,score:_},h)=>(p0(t,e,c,h)||(r+=_),r),0)/n.length}function u0(t,e,n,A,r){let c=[],_=h0(r.body.scoreThreshold,_5,t),h=r.body.nmsRadius^2;for(;c.lengthr.body.scoreThreshold&&c.push({keypoints:i,score:y})}return c}async function b0(t){return Promise.all(t.map(e=>e.buffer()))}function x5(t,e,n){return{score:t.score,keypoints:t.keypoints.map(({score:A,part:r,position:c})=>({score:A,part:r,position:{x:c.x*n,y:c.y*e}}))}}function g0(t,[e,n]){let A=t.squeeze(0),r=A.resizeBilinear([e,n]);return A.dispose(),r}function p1(t,[e,n],[A,r]){return t.map(_=>x5(_,e/A,n/r))}async function s5(t,e,n){return new Promise(async A=>{let r=t.shape[1],c=t.shape[2],_=await b0([e.heatmapScores,e.offsets,e.displacementFwd,e.displacementBwd]),h=_[0],x=_[1],o=_[2],i=_[3],y=await u0(h,x,o,i,n),a=p1(y,[r,c],[n.body.inputSize,n.body.inputSize]);A(a)})}async function y5(t,e,n){return new Promise(async A=>{let r=t.shape[1],c=t.shape[2],h=[await f0(e.heatmapScores,e.offsets,n)],x=p1(h,[r,c],[n.body.inputSize,n.body.inputSize]);A(x)})}var b1=class{constructor(e){this.baseModel=e}async estimatePoses(e,n){let A=g0(e,[n.body.inputSize,n.body.inputSize]),r=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await y5(e,r,n):await s5(e,r,n);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function g1(t){let e=await T0.loadGraphModel(t.body.modelPath),n=new s1(e);return t.debug&&p(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`),new b1(n)}var R1={};J(R1,{HandPose:()=>S1,load:()=>W1});var E1=v(R());var b=v(R());var v0=v(R());function ve(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function se(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function P0(t,e,n){let A=e.shape[1],r=e.shape[2],c=[[t.startPoint[1]/A,t.startPoint[0]/r,t.endPoint[1]/A,t.endPoint[0]/r]];return v0.image.cropAndResize(e,c,[0],n)}function z0(t,e){let n=[t.startPoint[0]*e[0],t.startPoint[1]*e[1]],A=[t.endPoint[0]*e[0],t.endPoint[1]*e[1]],r=t.palmLandmarks.map(c=>[c[0]*e[0],c[1]*e[1]]);return{startPoint:n,endPoint:A,palmLandmarks:r,confidence:t.confidence}}function Pe(t,e=1.5){let n=se(t),A=ve(t),r=[e*A[0]/2,e*A[1]/2],c=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:c,endPoint:_,palmLandmarks:t.palmLandmarks}}function ze(t){let e=se(t),n=ve(t),r=Math.max(...n)/2,c=[e[0]-r,e[1]-r],_=[e[0]+r,e[1]+r];return{startPoint:c,endPoint:_,palmLandmarks:t.palmLandmarks}}var T1=class{constructor(e,n,A){this.model=e,this.anchors=A.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=b.tensor2d(this.anchors),this.inputSizeTensor=b.tensor1d([n,n]),this.doubleInputSizeTensor=b.tensor1d([n*2,n*2])}normalizeBoxes(e){return b.tidy(()=>{let n=b.slice(e,[0,0],[-1,2]),A=b.slice(e,[0,2],[-1,2]),r=b.add(b.div(n,this.inputSizeTensor),this.anchorsTensor),c=b.div(A,this.doubleInputSizeTensor),_=b.mul(b.sub(r,c),this.inputSizeTensor),h=b.mul(b.add(r,c),this.inputSizeTensor);return b.concat2d([_,h],1)})}normalizeLandmarks(e,n){return b.tidy(()=>{let A=b.add(b.div(e.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return b.mul(A,this.inputSizeTensor)})}async getBoxes(e,n){let A=this.model.predict(e),r=A.squeeze();A.dispose();let c=b.tidy(()=>b.sigmoid(b.slice(r,[0,0],[-1,1])).squeeze()),_=c.dataSync(),h=b.slice(r,[0,1],[-1,4]),x=this.normalizeBoxes(h);h.dispose();let o=await b.image.nonMaxSuppressionAsync(x,_,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),i=o.arraySync();c.dispose(),o.dispose();let y=[];for(let a of i)if(_[a]>=n.hand.minConfidence){let d=b.slice(x,[a,0],[1,-1]),l=b.slice(r,[a,5],[1,14]),s=b.tidy(()=>this.normalizeLandmarks(l,a).reshape([-1,2]));l.dispose(),y.push({box:d,palmLandmarks:s,confidence:_[a]})}return r.dispose(),x.dispose(),y}async estimateHandBounds(e,n){let A=e.shape[1],r=e.shape[2],c=b.tidy(()=>e.resizeBilinear([n.hand.inputSize,n.hand.inputSize]).div(127.5).sub(1)),_=await this.getBoxes(c,n);c.dispose();let h=[];if(!_||_.length===0)return h;for(let x of _){let o=x.box.dataSync(),i=o.slice(0,2),y=o.slice(2,4),a=x.palmLandmarks.arraySync();x.box.dispose(),x.palmLandmarks.dispose(),h.push(z0({startPoint:i,endPoint:y,palmLandmarks:a,confidence:x.confidence},[r/n.hand.inputSize,A/n.hand.inputSize]))}return h}};var Re=v(R());function o5(t){return t-2*Math.PI*Math.floor((t+Math.PI)/(2*Math.PI))}function R0(t,e){let n=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return o5(n)}var E0=(t,e)=>[[1,0,t],[0,1,e],[0,0,1]];function Q(t,e){let n=0;for(let A=0;AP1([...c,1],n)),r=this.calculateLandmarksBoundingBox(A);return Pe(ze(r),a5)}getBoxForHandLandmarks(e){let n=this.calculateLandmarksBoundingBox(e),A=Pe(ze(n),W0);A.palmLandmarks=[];for(let r=0;r[_[0]*(d[0]-this.inputSize/2),_[1]*(d[1]-this.inputSize/2),_[2]*d[2]]),x=v1(A,[0,0]),o=h.map(d=>[...P1(d,x),d[2]]),i=S0(r),y=[...se(n),1],a=[Q(y,i[0]),Q(y,i[1])];return o.map(d=>[d[0]+a[0],d[1]+a[1],d[2]])}async estimateHands(e,n){let A=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,n),this.skipped=0),n.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(A=!0));let c=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let _=0;_=n.hand.minConfidence){let V=Re.reshape(I,[-1,3]),Y=V.arraySync();I.dispose(),V.dispose();let f=this.transformRawCoords(Y,d,x,a),w=this.getBoxForHandLandmarks(f);this.storedBoxes[_]=w;let m={landmarks:f,confidence:Z,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};c.push(m)}else this.storedBoxes[_]=null;I.dispose()}else{let x=Pe(ze(h),W0),o={confidence:h.confidence,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};c.push(o)}}return this.storedBoxes=this.storedBoxes.filter(_=>_!==null),this.detectedHands=c.length,c}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),A=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:r,endPoint:c}}};var 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$.loadGraphModel(t.body.modelPath),q.width=parseInt(q.signature.inputs["input_1:0"].tensorShape.dim[2].size),q.height=parseInt(q.signature.inputs["input_1:0"].tensorShape.dim[1].size),t.debug&&p(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`)),q}async function O1(t,e){if(!q||!e.body.enabled)return null;let n={width:t.shape[2],height:t.shape[1]},A=$.image.resizeBilinear(t,[q.width||e.body.inputSize,q.height||e.body.inputSize],!1),r=$.div(A,[255]);A.dispose();let c;if(e.profile){let h=await $.profile(()=>q.predict(r));c=h.result.find(x=>x.size===195).dataSync(),h.result.forEach(x=>x.dispose()),F("blazepose",h)}else{let h=await q.predict(r);c=h.find(x=>x.size===195||x.size===155).dataSync(),h.forEach(x=>x.dispose())}r.dispose();let _=[];for(let h=0;h{if(!t)return[];let e=[];for(let n=0;nx.part==="leftWrist"),r=t[n].keypoints.find(x=>x.part==="rightWrist"),c=t[n].keypoints.find(x=>x.part==="nose");c&&A&&r&&A.position.yx.part==="leftShoulder"),h=t[n].keypoints.find(x=>x.part==="rightShoulder");_&&h&&e.push({body:n,gesture:`leaning ${_.position.y>h.position.y?"left":"right"}`})}return e},I0=t=>{if(!t)return[];let e=[];for(let n=0;n0){let A=t[n].mesh[35][2]-t[n].mesh[263][2];Math.abs(A)<10?e.push({face:n,gesture:"facing camera"}):e.push({face:n,gesture:`facing ${A<0?"right":"left"}`}),Math.abs(t[n].mesh[374][1]-t[n].mesh[386][1])/Math.abs(t[n].mesh[443][1]-t[n].mesh[450][1])<.2&&e.push({face:n,gesture:"blink left eye"}),Math.abs(t[n].mesh[145][1]-t[n].mesh[159][1])/Math.abs(t[n].mesh[223][1]-t[n].mesh[230][1])<.2&&e.push({face:n,gesture:"blink right eye"});let _=Math.min(100,500*Math.abs(t[n].mesh[13][1]-t[n].mesh[14][1])/Math.abs(t[n].mesh[10][1]-t[n].mesh[152][1]));_>10&&e.push({face:n,gesture:`mouth ${Math.trunc(_)}% open`});let 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null}simmilarity(e,n){return this.config.face.embedding.enabled?$1(e,n):0}async load(e=null){this.state="load";let n=T();e&&(this.config=xe(this.config,e)),this.firstRun&&(this.config.debug&&u(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?qe:$0;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Be(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Qe(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?r1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?c1(this.config):null),this.models.posenet||(this.config.body.enabled?p1(this.config):null),this.models.handpose||(this.config.hand.enabled?M1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Be(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await 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backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&u("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||u("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&k1();try{await this.tf.setBackend(this.config.backend)}catch(r){u("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(T()-n)}}async detectFace(e){var o,i,s,a,d,l;let n,r,A,c,_,h=[];this.state="run:face",n=T();let x=await((o=this.models.face)==null?void 0:o.estimateFaces(e,this.config));this.perf.face=Math.trunc(T()-n);for(let y of x){if(this.analyze("Get Face"),!y.image||y.image.isDisposedInternal){u("Face object is disposed:",y.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?Ke(y.image,this.config):{}:(this.state="run:age",n=T(),r=this.config.face.age.enabled?await Ke(y.image,this.config):{},this.perf.age=Math.trunc(T()-n)),this.analyze("Start Gender:"),this.config.async?A=this.config.face.gender.enabled?$e(y.image,this.config):{}:(this.state="run:gender",n=T(),A=this.config.face.gender.enabled?await $e(y.image,this.config):{},this.perf.gender=Math.trunc(T()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?A1(y.image,this.config):{}:(this.state="run:emotion",n=T(),c=this.config.face.emotion.enabled?await A1(y.image,this.config):{},this.perf.emotion=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?_1(y.image,this.config):[]:(this.state="run:embedding",n=T(),_=this.config.face.embedding.enabled?await _1(y.image,this.config):[],this.perf.embedding=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.config.async&&([r,A,c,_]=await Promise.all([r,A,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((i=y==null?void 0:y.annotations)==null?void 0:i.leftEyeIris)&&((s=y==null?void 0:y.annotations)==null?void 0:s.rightEyeIris)&&(delete y.annotations.leftEyeIris,delete y.annotations.rightEyeIris);let j=((a=y.annotations)==null?void 0:a.leftEyeIris)&&((d=y.annotations)==null?void 0:d.rightEyeIris)?11.7*Math.max(Math.abs(y.annotations.leftEyeIris[3][0]-y.annotations.leftEyeIris[1][0]),Math.abs(y.annotations.rightEyeIris[4][1]-y.annotations.rightEyeIris[2][1])):0;h.push({confidence:y.confidence,faceConfidence:y.faceConfidence,boxConfidence:y.boxConfidence,box:y.box,mesh:y.mesh,boxRaw:y.boxRaw,meshRaw:y.meshRaw,annotations:y.annotations,age:r.age,gender:A.gender,genderConfidence:A.confidence,emotion:c,embedding:_,iris:j!==0?Math.trunc(j)/100:0}),(l=y.image)==null||l.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async r=>{var a,d,l,y;this.state="config";let A;this.config=xe(this.config,n),this.state="check";let c=this.sanity(e);c&&(u(c,e),r({error:c}));let _,h,x,o=T();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),A=T();let i=W0(e,this.config);if(!i||!i.tensor){u("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(T()-A),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(i.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",A=T(),x=this.config.face.enabled?await this.detectFace(i.tensor):[],this.perf.face=Math.trunc(T()-A)),this.analyze("Start Body:"),this.config.async?(_=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(i.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",A=T(),_=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(i.tensor,this.config)):[],this.perf.body=Math.trunc(T()-A)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(l=this.models.handpose)==null?void 0:l.estimateHands(i.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",A=T(),h=this.config.hand.enabled?await((y=this.models.handpose)==null?void 0:y.estimateHands(i.tensor,this.config)):[],this.perf.hand=Math.trunc(T()-A)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),i.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let s=[];this.config.gesture.enabled&&(A=T(),s=[...E0(x),...R0(_),...S0(h),...M0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(T()-A)),this.perf.total=Math.trunc(T()-o),this.state="idle",r({face:x,body:_,hand:h,gesture:s,performance:this.perf,canvas:i.canvas})})}async warmupBitmap(){let e=(A,c="application/octet-stream")=>fetch(`data:${c};base64,${A}`).then(_=>_.blob()),n,r;switch(this.config.warmup){case"face":n=await e(ze);break;case"full":n=await e(Re);break;default:n=null}if(n){let A=await createImageBitmap(n);r=await this.detect(A,this.config),A.close()}return r}async warmupCanvas(){return new Promise(e=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+ze;break;case"full":r=1200,n="data:image/jpeg;base64,"+Re;break;default:n=null}let A=new Image(r,r);A.onload=()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");c.width=r,c.height=r;let _=c.getContext("2d");_==null||_.drawImage(A,0,0);let h=_==null?void 0:_.getImageData(0,0,r,r);this.detect(h,this.config).then(x=>e(x))},n?A.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(ze):e(Re),r=Q0.node.decodeJpeg(n),A=r.expandDims(0);this.tf.dispose(r);let c=await this.detect(A,this.config);return this.tf.dispose(A),c}async warmup(e){let n=T();e&&(this.config=xe(this.config,e));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let A;typeof createImageBitmap=="function"?A=await this.warmupBitmap():typeof Image!="undefined"?A=await this.warmupCanvas():A=await this.warmupNode(),this.config.videoOptimized=r;let c=T();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(c-n),"ms",A),A}}; +2Q==`;var Z1={};J(Z1,{author:()=>K0,browser:()=>J0,bugs:()=>U0,default:()=>m5,dependencies:()=>$0,description:()=>H0,devDependencies:()=>tt,engines:()=>G0,homepage:()=>C0,keywords:()=>rt,license:()=>D0,main:()=>q0,module:()=>Y0,name:()=>L0,peerDependencies:()=>et,repository:()=>Q0,scripts:()=>nt,sideEffects:()=>F0,types:()=>B0,version:()=>V1});var L0="@vladmandic/human",V1="0.40.0",H0="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture 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--trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",lint:"eslint src demo server",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",update:"npm update --depth 20 --force && npm dedupe && npm prune && npm audit"},rt=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],m5={name:L0,version:V1,description:H0,sideEffects:F0,main:q0,module:Y0,browser:J0,types:B0,author:K0,bugs:U0,homepage:C0,license:D0,engines:G0,repository:Q0,dependencies:$0,peerDependencies:et,devDependencies:tt,scripts:nt,keywords:rt};var T=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function ye(...t){let e=n=>n&&typeof n=="object";return t.reduce((n,A)=>(Object.keys(A||{}).forEach(r=>{let c=n[r],_=A[r];Array.isArray(c)&&Array.isArray(_)?n[r]=c.concat(..._):e(c)&&e(_)?n[r]=ye(c,_):n[r]=_}),n),{})}var _t=class{constructor(e={}){this.tf=At,this.package=Z1,this.version=V1,this.config=ye(X0,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>I1(n,this.config),this.facemesh=ct,this.age=Ke,this.gender=De,this.emotion=n1,this.body=this.config.body.modelType.startsWith("posenet")?u1:N1,this.hand=R1}profile(){return this.config.profile?Ye:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,A=this.numTensors;this.numTensors=n;let r=n-A;r!==0&&p(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(e,n){return this.config.face.embedding.enabled?c0(e,n):0}async load(e=null){this.state="load";let n=T();e&&(this.config=ye(this.config,e)),this.firstRun&&(this.config.debug&&p(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("tf flags:",this.tf.ENV.flags)));let A=this.config.face.detector.modelPath.includes("faceboxes")?Je:ct;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?A.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Ue(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?e1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?c1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?h1(this.config):null),this.models.handpose||(this.config.hand.enabled?W1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?g1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?j1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await A.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Ue(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await e1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await c1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await h1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await W1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await g1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await j1(this.config))),this.firstRun&&(this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(T()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let n=T();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&p("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&p("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||p("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Y1();try{await this.tf.setBackend(this.config.backend)}catch(A){p("error: cannot set backend:",this.config.backend,A)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(p("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let A=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&p(`gl version:${A.getParameter(A.VERSION)} renderer:${A.getParameter(A.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(T()-n)}}async detectFace(e){var o,i,y,a,d,l;let n,A,r,c,_,h=[];this.state="run:face",n=T();let x=await((o=this.models.face)==null?void 0:o.estimateFaces(e,this.config));this.perf.face=Math.trunc(T()-n);for(let s of x){if(this.analyze("Get Face"),!s.image||s.image.isDisposedInternal){p("Face object is disposed:",s.image);continue}this.analyze("Start Age:"),this.config.async?A=this.config.face.age.enabled?Ce(s.image,this.config):{}:(this.state="run:age",n=T(),A=this.config.face.age.enabled?await Ce(s.image,this.config):{},this.perf.age=Math.trunc(T()-n)),this.analyze("Start Gender:"),this.config.async?r=this.config.face.gender.enabled?t1(s.image,this.config):{}:(this.state="run:gender",n=T(),r=this.config.face.gender.enabled?await t1(s.image,this.config):{},this.perf.gender=Math.trunc(T()-n)),this.analyze("Start Emotion:"),this.config.async?c=this.config.face.emotion.enabled?_1(s.image,this.config):{}:(this.state="run:emotion",n=T(),c=this.config.face.emotion.enabled?await _1(s.image,this.config):{},this.perf.emotion=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?_=this.config.face.embedding.enabled?x1(s.image,this.config):[]:(this.state="run:embedding",n=T(),_=this.config.face.embedding.enabled?await x1(s.image,this.config):[],this.perf.embedding=Math.trunc(T()-n)),this.analyze("End Emotion:"),this.config.async&&([A,r,c,_]=await Promise.all([A,r,c,_])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((i=s==null?void 0:s.annotations)==null?void 0:i.leftEyeIris)&&((y=s==null?void 0:s.annotations)==null?void 0:y.rightEyeIris)&&(delete s.annotations.leftEyeIris,delete s.annotations.rightEyeIris);let j=((a=s.annotations)==null?void 0:a.leftEyeIris)&&((d=s.annotations)==null?void 0:d.rightEyeIris)?11.7*Math.max(Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0]),Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])):0;h.push({confidence:s.confidence,faceConfidence:s.faceConfidence,boxConfidence:s.boxConfidence,box:s.box,mesh:s.mesh,boxRaw:s.boxRaw,meshRaw:s.meshRaw,annotations:s.annotations,age:A.age,gender:r.gender,genderConfidence:r.confidence,emotion:c,embedding:_,iris:j!==0?Math.trunc(j)/100:0}),(l=s.image)==null||l.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),h}async detect(e,n={}){return new Promise(async A=>{var a,d,l,s;this.state="config";let r;this.config=ye(this.config,n),this.state="check";let c=this.sanity(e);c&&(p(c,e),A({error:c}));let _,h,x,o=T();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=T();let i=I1(e,this.config);if(!i||!i.tensor){p("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(T()-r),this.analyze("Get Image:"),this.config.async?(x=this.config.face.enabled?this.detectFace(i.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=T(),x=this.config.face.enabled?await this.detectFace(i.tensor):[],this.perf.face=Math.trunc(T()-r)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(i.tensor,this.config):[]:_=this.config.body.enabled?O1(i.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",r=T(),this.config.body.modelType.startsWith("posenet")?_=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(i.tensor,this.config)):[]:_=this.config.body.enabled?await O1(i.tensor,this.config):[],this.perf.body=Math.trunc(T()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(l=this.models.handpose)==null?void 0:l.estimateHands(i.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=T(),h=this.config.hand.enabled?await((s=this.models.handpose)==null?void 0:s.estimateHands(i.tensor,this.config)):[],this.perf.hand=Math.trunc(T()-r)),this.analyze("End Hand:"),this.config.async&&([x,_,h]=await Promise.all([x,_,h])),i.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let y=[];this.config.gesture.enabled&&(r=T(),y=[...I0(x),...O0(_),...V0(h),...Z0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(T()-r)),this.perf.total=Math.trunc(T()-o),this.state="idle",A({face:x,body:_,hand:h,gesture:y,performance:this.perf,canvas:i.canvas})})}async warmupBitmap(){let e=(r,c="application/octet-stream")=>fetch(`data:${c};base64,${r}`).then(_=>_.blob()),n,A;switch(this.config.warmup){case"face":n=await e(Ee);break;case"full":n=await e(Me);break;default:n=null}if(n){let r=await createImageBitmap(n);A=await this.detect(r,this.config),r.close()}return A}async warmupCanvas(){return new Promise(e=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+Ee;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+Me;break;default:n=null}let r=new Image;r.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=r.naturalWidth,c.height=r.naturalHeight;let _=c.getContext("2d");_==null||_.drawImage(r,0,0);let h=await this.detect(c,this.config);e(h)},n?r.src=n:e(null)})}async warmupNode(){let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Ee):e(Me),A=At.node.decodeJpeg(n),r=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(r,this.config);return this.tf.dispose(r),c}async warmup(e){let n=T();e&&(this.config=ye(this.config,e));let A=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=A;let c=T();return this.config.debug&&p("Warmup",this.config.warmup,Math.round(c-n),"ms",r),r}}; //# sourceMappingURL=human.node.js.map diff --git a/dist/human.node.js.map b/dist/human.node.js.map index 4a85b9ab..44fcb6a4 100644 --- a/dist/human.node.js.map +++ b/dist/human.node.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-node.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/human.ts", "../src/log.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n 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0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n 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0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n 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0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-node.ts", "../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/human.ts", "../src/log.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/gesture/gesture.ts", "../src/image.ts", "../src/imagefx.js", "../config.js", "../src/sample.ts"], + "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 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0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n 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y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.4375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.node.json b/dist/human.node.json index be070006..747382cf 100644 --- a/dist/human.node.json +++ b/dist/human.node.json @@ -192,7 +192,7 @@ ] }, "src/posenet/modelBase.ts": { - "bytes": 1333, + "bytes": 1349, "imports": [ { "path": "dist/tfjs.esm.js", @@ -214,7 +214,7 @@ ] }, "src/posenet/keypoints.ts": { - "bytes": 2025, + "bytes": 2041, "imports": [] }, "src/posenet/vectors.ts": { @@ -240,7 +240,7 @@ ] }, "src/posenet/decodePose.ts": { - "bytes": 5152, + "bytes": 5182, "imports": [ { "path": "src/posenet/keypoints.ts", @@ -257,7 +257,7 @@ ] }, "src/posenet/decodeMultiple.ts": { - "bytes": 2301, + "bytes": 2323, "imports": [ { "path": "src/posenet/buildParts.ts", @@ -383,6 +383,23 @@ } ] }, + "src/blazepose/blazepose.ts": { + "bytes": 3327, + "imports": [ + { + "path": "src/log.ts", + "kind": "import-statement" + }, + { + "path": "dist/tfjs.esm.js", + "kind": "import-statement" + }, + { + "path": "src/profile.ts", + "kind": "import-statement" + } + ] + }, "src/gesture/gesture.ts": { "bytes": 4496, "imports": [] @@ -409,7 +426,7 @@ ] }, "config.js": { - "bytes": 10438, + "bytes": 10403, "imports": [] }, "src/sample.ts": { @@ -417,11 +434,11 @@ "imports": [] }, "package.json": { - "bytes": 2560, + "bytes": 2594, "imports": [] }, "src/human.ts": { - "bytes": 20100, + "bytes": 21042, "imports": [ { "path": "src/log.ts", @@ -467,6 +484,10 @@ "path": "src/handpose/handpose.ts", "kind": "import-statement" }, + { + "path": "src/blazepose/blazepose.ts", + "kind": "import-statement" + }, { "path": "src/gesture/gesture.ts", "kind": "import-statement" @@ -499,7 +520,7 @@ "imports": [], "exports": [], "inputs": {}, - "bytes": 708630 + "bytes": 715070 }, "dist/human.node-gpu.js": { "imports": [], @@ -510,13 +531,13 @@ "bytesInOutput": 551 }, "src/blazeface/facemesh.ts": { - "bytesInOutput": 1577 + "bytesInOutput": 1585 }, "src/posenet/keypoints.ts": { "bytesInOutput": 1677 }, "src/human.ts": { - "bytesInOutput": 10620 + "bytesInOutput": 11320 }, "src/log.ts": { "bytesInOutput": 251 @@ -540,7 +561,7 @@ "bytesInOutput": 28973 }, "src/faceboxes/faceboxes.ts": { - "bytesInOutput": 1613 + "bytesInOutput": 1618 }, "src/profile.ts": { "bytesInOutput": 604 @@ -561,7 +582,7 @@ "bytesInOutput": 1039 }, "src/posenet/modelBase.ts": { - "bytesInOutput": 656 + "bytesInOutput": 672 }, "src/posenet/heapSort.ts": { "bytesInOutput": 1017 @@ -570,7 +591,7 @@ "bytesInOutput": 454 }, "src/posenet/decodePose.ts": { - "bytesInOutput": 1282 + "bytesInOutput": 1271 }, "src/posenet/vectors.ts": { "bytesInOutput": 345 @@ -579,7 +600,7 @@ "bytesInOutput": 823 }, "src/posenet/decodeMultiple.ts": { - "bytesInOutput": 553 + "bytesInOutput": 525 }, "src/posenet/util.ts": { "bytesInOutput": 352 @@ -602,6 +623,9 @@ "src/handpose/anchors.ts": { "bytesInOutput": 126985 }, + "src/blazepose/blazepose.ts": { + "bytesInOutput": 1637 + }, "src/gesture/gesture.ts": { "bytesInOutput": 2391 }, @@ -612,16 +636,16 @@ "bytesInOutput": 10973 }, "config.js": { - "bytesInOutput": 1453 + "bytesInOutput": 1439 }, "src/sample.ts": { "bytesInOutput": 55295 }, "package.json": { - "bytesInOutput": 2569 + "bytesInOutput": 2593 } }, - "bytes": 277134 + "bytes": 279471 } } } diff --git a/dist/human.ts b/dist/human.ts index e3cbb0c2..52ef66aa 100644 --- a/dist/human.ts +++ b/dist/human.ts @@ -5,31 +5,31 @@ author: ' */ -var Human=(()=>{var C4=Object.create,Kc=Object.defineProperty,R4=Object.getPrototypeOf,F4=Object.prototype.hasOwnProperty,M4=Object.getOwnPropertyNames,$4=Object.getOwnPropertyDescriptor;var x1=e=>Kc(e,"__esModule",{value:!0});var Y2=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),lr=(e,t)=>{for(var n in t)Kc(e,n,{get:t[n],enumerable:!0})},O4=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of M4(t))!F4.call(e,r)&&r!=="default"&&Kc(e,r,{get:()=>t[r],enumerable:!(n=$4(t,r))||n.enumerable});return e},Zc=e=>e&&e.__esModule?e:O4(x1(Kc(e!=null?C4(R4(e)):{},"default",{value:e,enumerable:!0})),e);var 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this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if($h(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=D1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(D1(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=$h(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,m,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:c}=e,h=D1(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=T1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*_1(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Au||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*_1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=T1(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((u,c)=>{if(u==null){let h=n[c],d=nh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return u}),r(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=C1(e),n=new Set(t.map(a=>a.id));for(let a=0;a{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Xe,()=>"The result y returned by f() must be a tensor.");let s=tk(this.state.activeTape,t,a);if(!r&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Rg(e){return{info:[Lo,e,Cg].join(qr),topology:[Lo,e,Fk].join(qr),weightSpecs:[Lo,e,Mk].join(qr),weightData:[Lo,e,$k].join(qr),modelMetadata:[Lo,e,Ok].join(qr)}}function Dk(e){let t=e.split(qr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(qr)}function zk(e){return e.startsWith(Gs.URL_SCHEME)?e.slice(Gs.URL_SCHEME.length):e}var Gs=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Rg(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let 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i=JSON.parse(a);t.format=i.format,t.generatedBy=i.generatedBy,t.convertedBy=i.convertedBy,i.signature!=null&&(t.signature=i.signature),i.userDefinedMetadata!=null&&(t.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(t.modelInitializer=i.modelInitializer)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=_k(s),t}};Gs.URL_SCHEME="localstorage://";var Fg=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Gs.URL_SCHEME)?Pk(e.slice(Gs.URL_SCHEME.length)):null;vt.registerSaveRouter(Fg);vt.registerLoadRouter(Fg);function Pk(e){return new Gs(e)}var Lk=class{constructor(){F(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let 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e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};J().get("IS_NODE")&&J().setPlatform("node",new Gk);function Pe(e,t="float32",n){return t=t||"float32",v1(e),new Ft(e,t,n)}function qk(e,t){let n=R(e,"x","cast");if(!ag(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},a={dtype:t};return $.runKernel(Ja,r,a)}var fe=O({cast_:qk});function Xk(e){let t={x:R(e,"x","clone","string_or_numeric")};return $.runKernel(hs,t)}var Jn=O({clone_:Xk});function $g(e,t=!1){console.log(e.toString(t))}wg();var Kk={buffer:Pe,cast:fe,clone:Jn,print:$g};ok(Kk);var mn={};Oe(mn,{browserFiles:()=>Zk,browserHTTPRequest:()=>Jk,concatenateArrayBuffers:()=>L1,copyModel:()=>Vk,decodeWeights:()=>Ig,encodeWeights:()=>gk,fromMemory:()=>Qk,getLoadHandlers:()=>Tk,getModelArtifactsInfoForJSON:()=>xu,getSaveHandlers:()=>Sk,http:()=>H1,isHTTPScheme:()=>j1,listModels:()=>Wk,loadWeights:()=>Yk,moveModel:()=>Uk,registerLoadRouter:()=>Nk,registerSaveRouter:()=>Ik,removeModel:()=>Bk,weightsLoaderFactory:()=>Og,withSaveHandler:()=>e9});var t9="model",n9=".json",r9=".weights.bin";function Dg(e){return new Promise(t=>setTimeout(t)).then(e)}var Bo=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Bo.URL_SCHEME)&&(e=e.slice(Bo.URL_SCHEME.length)),(e==null||e.length===0)&&(e=t9),this.modelTopologyFileName=e+n9,this.weightDataFileName=e+r9}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment 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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await kr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(kr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Lo.print(this,e)}clone(){return this.throwIfDisposed(),Lo.clone(this)}toString(e=!1){let t=this.dataSync();return uk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Lo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),kr().makeVariable(this,e,t,n)}};Object.defineProperty(Ke,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return S1("Tensor",()=>Ke)}Z();var yu=class extends Ke{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!qr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);kr().disposeTensor(this),this.dataId=e.dataId,kr().incRef(this,null)}dispose(){kr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(yu,Symbol.hasInstance,{value:e=>e instanceof Ke&&e.assign!=null&&e.assign instanceof Function});var dr={};Oe(dr,{assertTypesMatch:()=>bg,getTensorsInContainer:()=>R1,isTensorInList:()=>fk,makeTypesMatch:()=>gt});var F1;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(F1||(F1={}));var M1;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(M1||(M1={}));var $1;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})($1||($1={}));var O1;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(O1||(O1={}));var D1;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(D1||(D1={}));var mk={float32:O1,int32:M1,bool:$1,complex64:D1};function Qn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return mk[e][t]}function Lh(e){return Qn(e,"int32")}function gt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Qn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function bg(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function fk(e,t){return t.some(n=>n.id===e.id)}function R1(e){let t=[],n=new Set;return vg(e,t,n),t}function vg(e,t,n){if(e==null)return;if(e instanceof Ke){t.push(e);return}if(!Ak(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),vg(s,t,n))}}function Ak(e){return Array.isArray(e)||typeof e=="object"}function z1(e){return e.kernelName!=null}var kg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},gu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new kg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Po(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof jl)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r(rthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return gu.nextTensorId++}nextVariableId(){return gu.nextVariableId++}clone(e){let t=$.runKernel(ds,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(Qa,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(Oh(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=z1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(z1(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=Oh(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,m,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:c}=e,h=z1(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=E1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&fa(e[0])&&(a=e.map(o=>pu(o)));let s=r.write(a,t,n),i=new Ke(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=cg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ke(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new yu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*b1(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof yu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*b1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=E1(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((u,c)=>{if(u==null){let h=n[c],d=rh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return u}),r(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=R1(e),n=new Set(t.map(a=>a.id));for(let a=0;a{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Ke,()=>"The result y returned by f() must be a tensor.");let s=ik(this.state.activeTape,t,a);if(!r&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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n=R(e,"labels","sigmoidCrossEntropyWithLogits"),r=R(t,"logits","sigmoidCrossEntropyWithLogits");tn(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Fr(r),s=P(r,n),i=td(Un(xt(Mt(r))));return se(me(a,s),i)}function XE(e,t,n,r=0,a=sn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"multiClassLabels","sigmoidCrossEntropy"),i=R(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","sigmoidCrossEntropy")),tn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let u=_e(r),c=_e(1),h=_e(.5);s=se(P(s,me(c,u)),P(h,u))}let l=qE(s,i);return Jr(l,o,a)}var KE=O({sigmoidCrossEntropy_:XE});function ZE(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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JE=O({softmaxCrossEntropy_:YE}),QE={fft:zu,ifft:al,rfft:Pu,irfft:md},eC={hammingWindow:YT,hannWindow:q5,frame:X5,stft:tE},je={flipLeftRight:sE,resizeNearestNeighbor:ex,resizeBilinear:Q5,rotateWithOffset:oE,cropAndResize:rE,nonMaxSuppression:uE,nonMaxSuppressionAsync:yE,nonMaxSuppressionWithScore:xE,nonMaxSuppressionWithScoreAsync:_E,nonMaxSuppressionPadded:vE,nonMaxSuppressionPaddedAsync:IE},nx={bandPart:EE,gramSchmidt:RE,qr:ME},tC={absoluteDifference:DE,computeWeightedLoss:Jr,cosineDistance:PE,hingeLoss:WE,huberLoss:VE,logLoss:HE,meanSquaredError:GE,sigmoidCrossEntropy:KE,softmaxCrossEntropy:JE},Qr=class extends s5{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Te(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return S5(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:_e(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Qr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var kd=class extends Qr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:B(()=>Be(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:B(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;B(()=>{let l=se(P(i,this.rho),P(st(s),1-this.rho)),u=P(ye(Yt(se(o,this.epsilon)),Yt(se(i,this.epsilon))),s),c=se(P(o,this.rho),P(st(u),1-this.rho));i.assign(l),o.assign(c);let h=se(P(u,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};kd.className="Adadelta";va(kd);var Id=class extends Qr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:B(()=>Tu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;B(()=>{let i=se(s,st(a));s.assign(i);let o=se(P(ye(a,Yt(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Id.className="Adagrad";va(Id);var Nd=class extends Qr{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=_e(t).variable(),this.accBeta2=_e(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=me(1,this.accBeta1),r=me(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:B(()=>Be(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:B(()=>Be(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedSecondMoment[s].variable,h=se(P(u,this.beta1),P(l,1-this.beta1)),d=se(P(c,this.beta2),P(st(l),1-this.beta2)),p=ye(h,n),m=ye(d,r);u.assign(h),c.assign(d);let f=se(P(ye(p,se(Yt(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),B(()=>{this.accBeta1.assign(Yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Yr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Nd.className="Adam";va(Nd);var Sd=class extends Qr{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=_e(0).variable(),this.accBeta1=_e(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=me(1,this.accBeta1),r=ye(-this.learningRate,se(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Sd.className="Adamax";va(Sd);var Lu=class extends Qr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];B(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Wt(_e(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Td.className="Momentum";va(Td);var Ed=class extends Qr{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:B(()=>Be(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:B(()=>Be(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:B(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;B(()=>{let l=se(P(i,this.decay),P(st(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,c=se(P(u,this.decay),P(s,1-this.decay)),h=ye(P(s,this.learningRate),Yt(me(l,se(st(c),this.epsilon)))),d=se(P(o,this.momentum),h);i.assign(l),u.assign(c),o.assign(d);let p=me(r,d);r.assign(p)}else{let 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Tx=rt(Xi,e=>e>=0?e:Math.exp(e)-1),yR={kernelName:Xi,backendName:"cpu",kernelFunc:Tx};function Ex(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;xe([a],"leakyRelu");let i=v.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=v.getTypedArrayFromDType("float32",i);for(let u=0;ue<0?t*e:e);function Cx(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;xe([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=xR(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var wR={kernelName:ks,backendName:"cpu",kernelFunc:Cx},Rx=rt(Is,e=>Math.max(0,e)),_R={kernelName:Is,backendName:"cpu",kernelFunc:Rx},Fx=rt(Ss,e=>Math.min(Math.max(0,e),6)),bR={kernelName:Ss,backendName:"cpu",kernelFunc:Fx};function rm(e,t,n,r,a){if(n==="linear")return Fr({inputs:{x:t},backend:e});if(n==="relu")return Rx({inputs:{x:t},backend:e});if(n==="elu")return Tx({inputs:{x:t},backend:e});if(n==="relu6")return Fx({inputs:{x:t},backend:e});if(n==="prelu")return Cx({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Ex({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function ft(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=v.sizeFromShape(a.shape),o=v.inferFromImplicitShape(s,i),l=v.sizeFromShape(o);v.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=Fr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),m=am(d,a.shape,a.dtype,p,c,"avg");h=n.makeTensorInfo(c.outShape,a.dtype,m.values)}return h}var eF={kernelName:Za,backendName:"cpu",kernelFunc:QR};function tF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;xe(a,"avgPool3d");let c=C.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=Ox(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var nF={kernelName:Xl,backendName:"cpu",kernelFunc:tF};function rF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;xe([a,s],"avgPool3DGrad");let c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,m=c.filterDepth,f=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,T=x-1-c.padInfo.left,E=w-1-c.padInfo.top,M=Pe(s.shape,"float32"),D=1/(m*f*A),L=n.bufferSync(a);for(let W=0;W=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce=c.outHeight||Math.floor(oe)!==oe))for(let pe=0;pe=c.outWidth||Math.floor(de)!==de||(te+=L.get(W,Q,oe,de,U))}}}M.set(te*D,W,j,X,G,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var aF={kernelName:ih,backendName:"cpu",kernelFunc:rF};function sF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;xe([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,_=g-1-c.padInfo.left,b=y-1-c.padInfo.top,w=Pe(i.shape,"float32"),x=1/(p*m),N=n.data.get(a.dataId).values,T=Pe(a.shape,"float32",N);for(let E=0;E=c.outHeight||Math.floor(G)!==G))for(let ee=0;ee=c.outWidth||Math.floor(Y)!==Y||(j+=T.get(E,G,Y,M))}}w.set(j*x,E,D,L,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var iF={kernelName:sh,backendName:"cpu",kernelFunc:sF};function oF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),xe([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let c=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(c.length),A=m.length,y=p.length,g=d.length,_=h.length,b=0,w=0,x=0,N=0;for(let T=0;T=A&&(b=0),w>=_&&(w=0),x>=y&&(x=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,f)}var lF={kernelName:us,backendName:"cpu",kernelFunc:oF};function uF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;xe([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=ft({inputs:{x:a},backend:n,attrs:{shape:l}}),m=nr({inputs:{x:p},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:c}}),A=ri({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var cF={kernelName:Kl,backendName:"cpu",kernelFunc:uF};function hF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Kf(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var dF={kernelName:oh,backendName:"cpu",kernelFunc:hF},pF=rt(Aa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new 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ft({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,d=Zf(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var gF={kernelName:ji,backendName:"cpu",kernelFunc:ul};function Dx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;xe([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new Ft(d.outShape,a.dtype),w=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=w[0],T=_?w[1]:w[2],E=_?w[2]:1,M=_?1:w[1],D=b.strides[0],L=_?b.strides[1]:b.strides[2],W=_?b.strides[2]:1,U=_?1:b.strides[1],j=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let pe=ce*x[0],de=Y+oe*T;for(let be=0;be=d.inWidth)continue;let Je=pe+$e*x[1],Qe=de+Fe*E,at=Je;for(let Ge=0;Ge=u.inDepth)continue;let ee=X*E[0],Y=D+G*T[1];for(let ae=0;ae=u.inHeight)continue;let oe=ee+Q*E[1],pe=Y+ce*T[2];for(let de=0;de=u.inWidth)continue;let Fe=oe+Re*E[2],Je=pe+$e*u.inChannels,Qe=Fe;for(let at=0;atMath.cos(e)),RF={kernelName:ns,backendName:"cpu",kernelFunc:CF},FF=rt(Hi,e=>Math.cosh(e)),MF={kernelName:Hi,backendName:"cpu",kernelFunc:FF};function $F(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=Pe([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=f>1?(D-E)*(h-1)/(f-1):0,j=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*j:.5*(M+L)*(d-1);if(ie<0||ie>d-1){for(let pe=0;pe1?M*(d-1)+ee*j:.5*(M+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;iey+m-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),m=n.data.get(a.dataId).values,f=new Float32Array(o*h*d*p),A=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. 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te=v.locToIndex([U,j,G,Y],L,v.computeStrides(M));W[te]=ae}}}return{dataId:l.write(v.toTypedArray(W,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},ZF={kernelName:yh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${yh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let 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vM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Dx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Lu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=rm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var kM={kernelName:Bs,backendName:"cpu",kernelFunc:vM};function IM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=zx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Lu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=rm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var NM={kernelName:Vs,backendName:"cpu",kernelFunc:IM};function SM(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=C.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Pe([u,c],r.dtype),p=n.data.get(a.dataId).values,m=n.data.get(r.dataId).values;for(let f=0;f=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;ge>=t?1:0),FM=Vt(cs,RM,null,"bool"),MM={kernelName:cs,backendName:"cpu",kernelFunc:FM};function $M(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=ft({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Lx(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var OM={kernelName:wh,backendName:"cpu",kernelFunc:$M},DM=rt(ro,e=>Number.isFinite(e)?1:0,"bool"),zM={kernelName:ro,backendName:"cpu",kernelFunc:DM},PM=rt(ao,e=>Math.abs(e)===Infinity?1:0,"bool"),LM={kernelName:ao,backendName:"cpu",kernelFunc:PM},WM=rt(so,e=>Number.isNaN(e)?1:0,"bool"),BM={kernelName:so,backendName:"cpu",kernelFunc:WM},VM=St((e,t)=>e<=t?1:0),UM=Vt(oo,VM,null,"bool"),jM={kernelName:oo,backendName:"cpu",kernelFunc:UM};function HM(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=hx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var GM={kernelName:bh,backendName:"cpu",kernelFunc:HM},qM=rt(lo,e=>Math.log1p(e)),XM={kernelName:lo,backendName:"cpu",kernelFunc:qM},KM=St((e,t)=>e&&t),ZM=Vt(uo,KM,null,"bool"),YM={kernelName:uo,backendName:"cpu",kernelFunc:ZM},JM=rt(eu,e=>e?0:1,"bool"),QM={kernelName:eu,backendName:"cpu",kernelFunc:JM},e$=St((e,t)=>e||t),t$=Vt(tu,e$,null,"bool"),n$={kernelName:tu,backendName:"cpu",kernelFunc:t$};function r$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;xe(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function m(f){let A=f%u,y=f-A+Math.max(0,A-s),g=f-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=n.bufferSync(s),d=JR(h,c),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,A=c.dilationDepth,y=c.dilationHeight,g=c.dilationWidth,_=c.effectiveFilterDepth,b=c.effectiveFilterHeight,w=c.effectiveFilterWidth,x=_-1-c.padInfo.front,N=w-1-c.padInfo.left,T=b-1-c.padInfo.top,E=Pe(s.shape,"float32"),M=n.bufferSync(a);for(let D=0;D=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie=c.outHeight||Math.floor(Q)!==Q))for(let ce=0;ce=c.outWidth||Math.floor(oe)!==oe)continue;let pe=_*b*w-1-d.get(D,te,Q,oe,L),de=ae*b*w+ie*w+ce,be=pe===de?1:0;be!==0&&(Y+=M.get(D,te,Q,oe,L)*be)}}}E.set(Y,D,W,U,j,L)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var p$={kernelName:Ih,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;xe([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=n.data.get(o.dataId).values,m=Pe(d.outShape,o.dtype,$x(p,o.shape,o.dtype,d).values),f=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,_=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,x=_-1-d.padInfo.top,N=Pe(o.shape,"float32"),T=n.data.get(a.dataId).values,E=Pe(a.shape,"float32",T);for(let M=0;M=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y=d.outWidth||Math.floor(ae)!==ae)continue;let te=_*b-1-m.get(M,ee,ae,D),ie=G*b+Y,Q=te===ie?1:0;Q!==0&&(X+=E.get(M,ee,ae,D)*Q)}}N.set(X,M,L,W,D)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var m$={kernelName:kh,backendName:"cpu",kernelFunc:f$};function A$(e,t,n,r,a){let s=v.computeStrides(t),i=am(e,t,n,s,a,"max"),o=$x(e,t,n,a,!0,r);return[i.values,o.values]}var y$={kernelName:Nh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;xe(r,"MaxPoolWithArgmax");let u=l.data.get(r.dataId).values,c=C.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=A$(u,r.shape,r.dtype,o,c),p=l.write(h,c.outShape,r.dtype),m=l.write(d,c.outShape,r.dtype);return[{dataId:p,shape:c.outShape,dtype:r.dtype},{dataId:m,shape:c.outShape,dtype:"int32"}]}};function $d(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;xe(a,"sum");let o;a.dtype==="bool"?o=Ea({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Fr({inputs:{x:a},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),c=C.getAxesPermutation(u,l),h=u,d=o;c!=null&&(d=nr({inputs:{x:o},backend:n,attrs:{perm:c}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,m]=C.computeOutAndReduceShapes(d.shape,h),f=C.upcastType(d.dtype,"int32"),A=Fd(n,p,f),y=v.sizeFromShape(m),g=n.data.get(A.dataId).values,_=n.data.get(d.dataId).values;for(let b=0;bn.disposeIntermediateTensorInfo(f)),m}var w$={kernelName:ys,backendName:"cpu",kernelFunc:x$};function _$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;xe(a,"min");let o=v.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=nr({inputs:{x:a},backend:n,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",l,c.shape.length);let[h,d]=C.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),f=n.data.get(c.dataId).values;for(let y=0;yg[0]+a.shape[_]+g[1]),l=s.map(g=>g[0]),u=s.map((g,_)=>g[0]+a.shape[_]),c=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=v.computeStrides(a.shape),m=v.sizeFromShape(o),f=o.length,A=v.computeStrides(o),y=v.getTypedArrayFromDType(a.dtype,m);for(let g=0;g=u[w]&&(_[w]=(u[w]-1)*2-_[w]+c);_=_.map((w,x)=>w-l[x]);let b=v.locToIndex(_,d,p);y[g]=h[b]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var k$={kernelName:au,backendName:"cpu",kernelFunc:v$},I$=St((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),N$=Vt(co,I$),S$={kernelName:co,backendName:"cpu",kernelFunc:N$},T$=Mi(s8());function Bx(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,m=c.filterDepth,f=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,T=x-1-c.padInfo.left,E=w-1-c.padInfo.top,M=Pe(s.shape,"float32"),D=1/(m*f*A),L=n.bufferSync(a);for(let W=0;W=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce=c.outHeight||Math.floor(oe)!==oe))for(let pe=0;pe=c.outWidth||Math.floor(de)!==de||(te+=L.get(W,Q,oe,de,U))}}}M.set(te*D,W,H,X,G,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var uF={kernelName:oh,backendName:"cpu",kernelFunc:lF};function cF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;we([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,_=g-1-c.padInfo.left,b=y-1-c.padInfo.top,w=Pe(i.shape,"float32"),x=1/(p*m),N=n.data.get(a.dataId).values,T=Pe(a.shape,"float32",N);for(let E=0;E=c.outHeight||Math.floor(G)!==G))for(let ee=0;ee=c.outWidth||Math.floor(Y)!==Y||(H+=T.get(E,G,Y,M))}}w.set(H*x,E,D,L,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var hF={kernelName:ih,backendName:"cpu",kernelFunc:cF};function dF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=ft({inputs:{x:a},backend:n,attrs:{shape:l}}),m=ar({inputs:{x:p},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:c}}),A=ai({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var mF={kernelName:Zl,backendName:"cpu",kernelFunc:fF};function AF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Zf(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var yF={kernelName:lh,backendName:"cpu",kernelFunc:AF},gF=rt(ya,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>v.sizeFromShape(f.shape)>0);if(o.length===1)return $r({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(b=>ri({inputs:{input:b},backend:n})),A=o.map(b=>ul({inputs:{input:b},backend:n})),y=cl({inputs:f,backend:n,attrs:{axis:s}}),g=cl({inputs:A,backend:n,attrs:{axis:s}}),_=Rn({inputs:{real:y,imag:g},backend:n});return f.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),_}let u=o.map(f=>{let A=v.sizeFromShape(f.shape.slice(s));return ft({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,d=Yf(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var vF={kernelName:ji,backendName:"cpu",kernelFunc:cl};function Bx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;we([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new Ft(d.outShape,a.dtype),w=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=w[0],T=_?w[1]:w[2],E=_?w[2]:1,M=_?1:w[1],D=b.strides[0],L=_?b.strides[1]:b.strides[2],W=_?b.strides[2]:1,U=_?1:b.strides[1],H=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let pe=ce*x[0],de=Y+oe*T;for(let be=0;be=d.inWidth)continue;let Je=pe+$e*x[1],Qe=de+Fe*E,at=Je;for(let qe=0;qe=u.inDepth)continue;let ee=X*E[0],Y=D+G*T[1];for(let ae=0;ae=u.inHeight)continue;let oe=ee+Q*E[1],pe=Y+ce*T[2];for(let de=0;de=u.inWidth)continue;let Fe=oe+Re*E[2],Je=pe+$e*u.inChannels,Qe=Fe;for(let at=0;atMath.cos(e)),DF={kernelName:rs,backendName:"cpu",kernelFunc:OF},zF=rt(Gi,e=>Math.cosh(e)),PF={kernelName:Gi,backendName:"cpu",kernelFunc:zF};function LF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=Pe([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=f>1?(D-E)*(h-1)/(f-1):0,H=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*H:.5*(M+L)*(d-1);if(ie<0||ie>d-1){for(let pe=0;pe1?M*(d-1)+ee*H:.5*(M+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;iey+m-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),m=n.data.get(a.dataId).values,f=new Float32Array(o*h*d*p),A=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:A,dilationWidth:y,padInfo:g}=p,_=g.left,b=g.top,w=p.outChannels/p.inChannels,x=new Ft(p.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=x.values;for(let M=0;M=p.inHeight)continue;let ee=X*h[0],Y=D+G*c[1];for(let ae=0;ae=p.inWidth)continue;let oe=ee+Q*h[1],pe=Y+ce*p.inChannels,de=te,be=oe;for(let ke=0;ke{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:m,inWidth:f,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:b,strideWidth:w,filterHeight:x,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:M}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),D=v.sizeFromShape(M),L=M.length,W=v.getArrayFromDType(r.dtype,D);for(let U=0;U=0&&Q=0&&oeae&&(ae=be)}}}let te=v.locToIndex([U,H,G,Y],L,v.computeStrides(M));W[te]=ae}}}return{dataId:l.write(v.toTypedArray(W,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},tM={kernelName:gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${gh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let L=0;L=0&&ie=0&&ceee&&(ee=oe,Y=te,ae=Q)}}}D[Y][ae][G]+=M[L][W][H][G]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},nM={kernelName:yh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${yh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L=0&&ie=0&&ceee&&(ee=oe,Y=ie,ae=ce)}}}D[L][Y][ae][G]+=M[L][W][H][G]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function rM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;we([r,a],"eluGrad");let s=new Float32Array(v.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(a.shape,"float32",s)}var aM={kernelName:xh,backendName:"cpu",kernelFunc:rM},sM=Tt((e,t)=>e===t?1:0),Ux=Vt(Yi,sM,null,"bool"),iM={kernelName:Yi,backendName:"cpu",kernelFunc:Ux},oM=C.ERF_P,lM=C.ERF_A1,uM=C.ERF_A2,cM=C.ERF_A3,hM=C.ERF_A4,dM=C.ERF_A5,pM=rt(Zi,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+oM*n);return t*(1-((((dM*r+hM)*r+cM)*r+uM)*r+lM)*r*Math.exp(-n*n))}),fM={kernelName:Zi,backendName:"cpu",kernelFunc:pM};function $d(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:a},backend:n,attrs:{shape:o}})}var mM={kernelName:Ji,backendName:"cpu",kernelFunc:$d},AM=Tt((e,t)=>e/t),im=Vt(is,AM),om={kernelName:is,backendName:"cpu",kernelFunc:im};function Hx(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[a,s],c=v.sizeFromShape(u),h=v.getTypedArrayFromDType("float32",c),d=v.getTypedArrayFromDType("float32",c);for(let A=0;A{let{image:r}=e,a=n,s=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[i,o,l,u]=r.shape,c=a.data.get(r.dataId).values;for(let h=0;h=0&&_Math.floor(e/t)),NM=Vt(us,IM,null,"int32"),SM={kernelName:us,backendName:"cpu",kernelFunc:NM};function TM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Bx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Wu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=am(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var EM={kernelName:Vs,backendName:"cpu",kernelFunc:TM};function CM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Vx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=Wu({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=am(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var RM={kernelName:Us,backendName:"cpu",kernelFunc:CM};function FM(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=C.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Pe([u,c],r.dtype),p=n.data.get(a.dataId).values,m=n.data.get(r.dataId).values;for(let f=0;f=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;ge>=t?1:0),zM=Vt(hs,DM,null,"bool"),PM={kernelName:hs,backendName:"cpu",kernelFunc:zM};function LM(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=ft({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Hx(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var WM={kernelName:_h,backendName:"cpu",kernelFunc:LM},BM=rt(ao,e=>Number.isFinite(e)?1:0,"bool"),VM={kernelName:ao,backendName:"cpu",kernelFunc:BM},UM=rt(so,e=>Math.abs(e)===Infinity?1:0,"bool"),HM={kernelName:so,backendName:"cpu",kernelFunc:UM},jM=rt(io,e=>Number.isNaN(e)?1:0,"bool"),GM={kernelName:io,backendName:"cpu",kernelFunc:jM},qM=Tt((e,t)=>e<=t?1:0),XM=Vt(lo,qM,null,"bool"),KM={kernelName:lo,backendName:"cpu",kernelFunc:XM};function ZM(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Ax(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var YM={kernelName:vh,backendName:"cpu",kernelFunc:ZM},JM=rt(uo,e=>Math.log1p(e)),QM={kernelName:uo,backendName:"cpu",kernelFunc:JM},e$=Tt((e,t)=>e&&t),t$=Vt(co,e$,null,"bool"),n$={kernelName:co,backendName:"cpu",kernelFunc:t$},r$=rt(tu,e=>e?0:1,"bool"),a$={kernelName:tu,backendName:"cpu",kernelFunc:r$},s$=Tt((e,t)=>e||t),i$=Vt(nu,s$,null,"bool"),o$={kernelName:nu,backendName:"cpu",kernelFunc:i$};function l$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;we(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function m(f){let A=f%u,y=f-A+Math.max(0,A-s),g=f-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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e[1]===t[1]&&Bd(e[0])&&Bd(t[0])}var Vd,Ud;function Aw(e){if(Vd==null){let t=Or(e);Vd=t.getParameter(t.MAX_TEXTURE_SIZE)}return Vd}function fD(){Vd=null}function mD(){Ud=null}function yw(e){if(Ud==null){let t=Or(e);Ud=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Ud)}function gw(e){if(e===0)return 0;let t,n=Or(e);return jn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:jn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function jn(e,t){return e.getExtension(t)!=null}function dm(e){try{if(Or(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function xw(e){if(e===0)return!1;let t=Or(e);if(e===1){if(!jn(t,"OES_texture_float"))return!1}else if(!jn(t,"EXT_color_buffer_float"))return!1;return mm(t)}function ww(e){if(e===0)return!1;let t=Or(e);if(e===1){if(!jn(t,"OES_texture_float")||!jn(t,"WEBGL_color_buffer_float"))return!1}else{if(jn(t,"EXT_color_buffer_float"))return mm(t);let n="EXT_color_buffer_half_float";if(jn(t,n)){let r=t.getExtension(n);return ID(t,r)}return!1}return mm(t)}function mm(e){let t=fm(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function ID(e,t){let n=fm(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function _w(e){return e!==2?!1:Or(e).fenceSync!=null}function hl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>dm(2)?2:dm(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>Aw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>yw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:gw(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Wh.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>xw(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ww(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>_w(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function on(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=` bool isnan_custom(float val) { return (val > 0.0 || val < 0.0) ? false : val != 0.0; } @@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function oi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function mm(e){let t=v.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function li(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Am(e){let t=v.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}var gw=` +`}var vw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return c / 255.0; } -`,_D=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Uu.DENSE;let t=Hu(e),n=on();this.outputShape=e,this.userCode=` +`,ND=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE;let t=Gu(e),n=on();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${li(["r","c","d"],e)} return ivec3(r, c, d); } @@ -138,9 +138,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},bD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Uu.DENSE;let t=Hu(e),n=on();this.outputShape=e,this.userCode=` + `}},SD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE;let t=Gu(e),n=on();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${oi(["r","c","d"],e)} + ${li(["r","c","d"],e)} return ivec3(r, c, d); } @@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${n.output} = result; } - `}},vD=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Hn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` - ${gw} + `}},TD=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Gn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` + ${vw} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},kD=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Hn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` - ${gw} + `}},ED=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Gn.DOWNLOAD;let t=on();this.outputShape=e,this.userCode=` + ${vw} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},ID=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=on(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` - ${mm(e)} + `}},CD=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=on(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` + ${Am(e)} void main() { ivec3 coords = getOutputCoords(); @@ -204,7 +204,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${r.output} = vec4(${i}, 0., 0., 0.); } - `}},ND=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=on(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` + `}},RD=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=on(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=` localCoords = coords; if(localCoords[2] + ${u} < ${e[2]}) { localCoords[2] += ${u}; @@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee } } `}this.userCode=` - ${mm(e)} + ${Am(e)} void main() { ivec3 coords = getOutputCoords(); @@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ${r.output} = ${o}; } - `}},xw={};Oe(xw,{bindVertexProgramAttributeStreams:()=>Tw,createBufferFromOutputTexture:()=>Rw,createFloat16MatrixTexture:()=>kw,createFloat16PackedMatrixTexture:()=>Sw,createFloat32MatrixTexture:()=>vw,createIndexBuffer:()=>bw,createPackedMatrixTexture:()=>Nw,createUnsignedBytesMatrixTexture:()=>Iw,createVertexBuffer:()=>_w,createVertexShader:()=>ww,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Mw,downloadFloat32MatrixFromBuffer:()=>Fw,downloadMatrixFromPackedOutputTexture:()=>Ow,downloadPackedMatrixFromBuffer:()=>$w,getInternalFormatForFloat16MatrixTexture:()=>ym,getInternalFormatForFloat16PackedMatrixTexture:()=>wm,getInternalFormatForFloat32MatrixTexture:()=>Am,getInternalFormatForPackedMatrixTexture:()=>xm,getInternalFormatForUnsignedBytesMatrixTexture:()=>gm,uploadDenseMatrixToTexture:()=>Ew,uploadPixelDataToTexture:()=>Cw});function ww(e){let t=on(),n=`${t.version} + `}},kw={};Oe(kw,{bindVertexProgramAttributeStreams:()=>Mw,createBufferFromOutputTexture:()=>Dw,createFloat16MatrixTexture:()=>Ew,createFloat16PackedMatrixTexture:()=>Fw,createFloat32MatrixTexture:()=>Tw,createIndexBuffer:()=>Sw,createPackedMatrixTexture:()=>Rw,createUnsignedBytesMatrixTexture:()=>Cw,createVertexBuffer:()=>Nw,createVertexShader:()=>Iw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Pw,downloadFloat32MatrixFromBuffer:()=>zw,downloadMatrixFromPackedOutputTexture:()=>Ww,downloadPackedMatrixFromBuffer:()=>Lw,getInternalFormatForFloat16MatrixTexture:()=>gm,getInternalFormatForFloat16PackedMatrixTexture:()=>_m,getInternalFormatForFloat32MatrixTexture:()=>ym,getInternalFormatForPackedMatrixTexture:()=>wm,getInternalFormatForUnsignedBytesMatrixTexture:()=>xm,uploadDenseMatrixToTexture:()=>$w,uploadPixelDataToTexture:()=>Ow});function Iw(e){let t=on(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. 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this.throwIfDisposed(),Fw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Rw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(hm(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Pw(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Lw(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return zw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Dw(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ww(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=nw(t,e),r=Iw(t),a=rw(t);return ge(t,()=>t.attachShader(a,r)),ge(t,()=>t.attachShader(a,n)),aw(t,a),this.debug&&zd(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Mw(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ge(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&zd(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?hw(this.gl,e,t):dw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ge(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),pw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=dl(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&zd(this.gl,this.program),Vu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ge(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ge(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Bu(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=FD(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Pd(this.gl,e,this.framebuffer),this.debug&&Vu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Pd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Vu(this.gl)):hm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Pd(r,e,this.framebuffer),this.debug&&Vu(r),this.outputTexture=e,ge(r,()=>r.viewport(0,0,t,n)),ge(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ge(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function FD(e){let t=0;for(;t{let m=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${m>1?`[${m}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(` +`),i=e.map(p=>MD(p,t,r)).join(` +`),o=t.texShape,l=on(),u=DD(l),c,h,d=LD(l);return t.isPacked?(c=$D(t.logicalShape,o),h=PD(l)):(c=OD(t.logicalShape,o),h=zD(l)),r&&(d+=WD),[d,u,h,s,c,i,n].join(` +`)}function pl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return VD(e);case 1:return UD(e);case 2:return HD(e);case 3:return jD(e);case 4:return GD(e);case 5:return qD(e);case 6:return XD(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Vw(e){switch(e.shapeInfo.logicalShape.length){case 0:return KD(e);case 1:return ZD(e);case 2:return YD(e);case 3:return JD(e);default:return QD(e)}}function MD(e,t,n=!1){let r="";n?r+=Vw(e):r+=pl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=ez(e,t):r+=tz(e,t)),r}function $D(e,t){switch(e.length){case 0:return Uw();case 1:return nz(e,t);case 2:return sz(e,t);case 3:return rz(e,t);default:return az(e,t)}}function OD(e,t){switch(e.length){case 0:return Uw();case 1:return iz(e,t);case 2:return hz(e,t);case 3:return oz(e,t);case 4:return lz(e,t);case 5:return uz(e,t);case 6:return cz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function DD(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function FD(e){return` + `}function zD(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function MD(e){return` + `}function PD(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function $D(e){return`${e.version} + `}function LD(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return fract((p3.x + p3.y) * p3.z); } - ${oz} - ${lz} - ${uz} - `}var oz=` + ${dz} + ${pz} + ${fz} + `}var dz=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,lz=` +`,pz=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,uz=` +`,fz=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,OD=` +`,WD=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function Pw(){return` +`;function Uw(){return` int getOutputCoords() { return 0; } - `}function YD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?` + `}function nz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?` int getOutputCoords() { return 2 * int(resultUV.x * ${n[1]}.0); } @@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function tz(e,t){return t[0]===1?` + `}function iz(e,t){return t[0]===1?` int getOutputCoords() { return int(resultUV.x * ${t[1]}.0); } @@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function JD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return` + `}function rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]})); @@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function nz(e,t){let n=oi(["r","c","d"],e);return` + `}function oz(e,t){let n=li(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function QD(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(` + `}function ez(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Bw(e.shapeInfo.logicalShape,t.logicalShape),l=it(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(` `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)p=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?p=` @@ -804,22 +804,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${r}(${d}); ${p} } - `}function ZD(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` + `}function tz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` float ${a}() { return sampleTexture(${n}, resultUV); } - `;let u=it(l),c=Dw(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(f=>`coords.${p[f+h]} = 0;`).join(` + `;let u=it(l),c=Bw(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(f=>`coords.${p[f+h]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${p[A+h]}`).join(", "),` float ${a}() { ${u} coords = getOutputCoords(); ${d} return get${r}(${m}); } - `}function it(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function fl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ml(e,t){return t.map(n=>e[n]).join(", ")}function cz(e,t,n,r){let a=t.userCode,s=n.map((p,m)=>{let f={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(f.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=DD(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. 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o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:gz,bincountImpl:jw,bincountReduceImpl:xz,ceilImpl:wz,concatImpl:_z,expImpl:bz,expm1Impl:vz,floorImpl:kz,gatherV2Impl:Iz,greaterImpl:Nz,lessImpl:Sz,linSpaceImpl:Tz,logImpl:Ez,maxImpl:Cz,maximumImpl:Rz,minimumImpl:Fz,multiplyImpl:Mz,negImpl:$z,prodImpl:Oz,rangeImpl:Dz,rsqrtImpl:zz,simpleAbsImpl:Gw,sliceImpl:Pz,stridedSliceImpl:Lz,subImpl:Wz,tileImpl:Bz,topKImpl:Vz,transposeImpl:bm,uniqueImpl:Uz}=Kf;function qw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:qw(e,t)}function Hz(e,t){if(e===1)return"rc";let n="";for(let r=0;r ${t[0]}`;let r="";for(let a=e-2;a= ${t[a]}`,a ${t[0]}`;let r="";for(let a=e-2;a= ${t[a]}`,a= ${t}; bool rEdge = rp1 >= ${n}; - `}function Vz(e,t){let n=e.length,r=jz(n,t);return n===1?`getA(rc), + `}function qz(e,t){let n=e.length,r=Kz(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${r[0]}), cEdge ? 0. : getA(${r[1]}), rEdge ? 0. : getA(${r[2]}), - rEdge || cEdge ? 0. : getA(${r[3]})`}var Uw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${r[3]})`}var Xw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=` ${a} ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${r>0?"}":""} `}this.userCode=` - ${Hz(t)} - ${mm(e)} + ${Zz(t)} + ${Am(e)} void main() { ivec3 rc = getOutputCoords(); @@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Hz(e){return` + `}};function Zz(e){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${oi(["r","c","d"],e)} + ${li(["r","c","d"],e)} return ivec3(r, c, d); } - `}var Gz=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Hw(t,n),a=Gw(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=jw(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Jt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Jt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Jt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Hw(n,r),s=Gw(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=jw(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qz(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function jw(e,t,n,r,a){let s=Xz(t,r),i;if(a){let[l,u]=hl(e[0],e[1]);i=l*u}else{let[l,u]=ju(e[0],e[1]);i=l*u}let o=qz(n,s);return i*o}function Xz(e,t){switch(e){case Jt.PACKED_2X2_FLOAT32:return xm(t);case Jt.PACKED_2X2_FLOAT16:return wm(t);case Jt.UNPACKED_FLOAT32:return Am(t);case Jt.UNPACKED_FLOAT16:return ym(t);case Jt.PACKED_4X1_UNSIGNED_BYTE:return gm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Kz(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Jt.PACKED_2X2_FLOAT32:Jt.UNPACKED_FLOAT32:e?Jt.PACKED_2X2_FLOAT16:Jt.UNPACKED_FLOAT16}function Hw(e,t){if(e===Hn.UPLOAD)return Jt.PACKED_2X2_FLOAT32;if(e===Hn.RENDER||e==null)return Kz(t);if(e===Hn.DOWNLOAD||e===Hn.PIXELS)return Jt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Gw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ca=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=` + `}var Yz=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Zw(t,n),a=Yw(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Kw(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Jt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Jt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Jt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Jt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Zw(n,r),s=Yw(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Kw(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Jz(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Kw(e,t,n,r,a){let s=Qz(t,r),i;if(a){let[l,u]=dl(e[0],e[1]);i=l*u}else{let[l,u]=ju(e[0],e[1]);i=l*u}let o=Jz(n,s);return i*o}function Qz(e,t){switch(e){case Jt.PACKED_2X2_FLOAT32:return wm(t);case Jt.PACKED_2X2_FLOAT16:return _m(t);case Jt.UNPACKED_FLOAT32:return ym(t);case Jt.UNPACKED_FLOAT16:return gm(t);case Jt.PACKED_4X1_UNSIGNED_BYTE:return xm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Jt.PACKED_2X2_FLOAT32:Jt.UNPACKED_FLOAT32:e?Jt.PACKED_2X2_FLOAT16:Jt.UNPACKED_FLOAT16}function Zw(e,t){if(e===Gn.UPLOAD)return Jt.PACKED_2X2_FLOAT32;if(e===Gn.RENDER||e==null)return eP(t);if(e===Gn.DOWNLOAD||e===Gn.PIXELS)return Jt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Yw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ra=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=` float unaryOperation(float x) { ${t} } @@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},dr="if (isnan(x)) return x;",Zz="return x;",qw="return abs(x);",Yz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Jz=dr+` + `}},fr="if (isnan(x)) return x;",tP="return x;",Jw="return abs(x);",nP="return (x >= 0.0) ? x : (exp(x) - 1.0);",rP=fr+` return (x < 0.0) ? 0.0 : x; -`,Qz=dr+` +`,aP=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,jd="return x;",eP="return x;",tP=` +`,jd="return x;",sP="return x;",iP=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,nP=` +`,oP=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,rP=` +`,lP=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Al=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` +`,yl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},aP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=ln("rc",t),r=it(t),a=Lz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},uP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=ln("rc",t),r=it(t),a=Hz(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${r} rc = getOutputCoords(); vec4 packedInput = getA(${a}); setOutput(getChannel(packedInput, ${i})); } - `}},sP=Rr.whereImpl,iP=1e-7,oP=1e-4,bm={};function lP(e){return e in bm||(bm[e]={}),bm[e]}var uP=128,cP=600;function hP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*cP/1024/1024}var yl=class extends jl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Mr(J().getNumber("WEBGL_VERSION"));this.binaryCache=lP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Ud(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Gz(this.gpgpu),this.numMBBeforeWarning=hP(),this.texData=new Qc(this,Ir())}nextDataId(){return yl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Hn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Hn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Al(i,jd):h=new Ca(i,jd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(m=>p.push(m))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Al(r,jd):p=new Ca(r,jd);let m=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Hu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=p[0],f=p[1];c=C.mergeRealAndImagArrays(m,f)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ir().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ir().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=uP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Ir().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new aP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Uz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ai(e.shape),...si(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[ai(t),...si(t)],s=new Uw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Pd(r),i;n?i=new bD(s):i=new _D(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Uu.DENSE){let f=Hu(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Vu(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=dz(e,l,u),h=this.getAndSaveBinary(c,()=>cz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),hz(this.gpgpu,h,l,u,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=v.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(_e(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?iP:oP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=cw(n,o),t.texShape=c),a!=null){let h=Pd(n),d,p=c[1],m=c[0],f=a instanceof Uint8Array;o?([p,m]=hl(c[0],c[1]),d=new ND(h,[m,p],f)):d=new ID(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Hn.PIXELS:this.texData.get(A.dataId).usage=Hn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=dP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};yl.nextDataId=0;function dP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew yl,2);var pP={forceHalfFloat:Kw},Zw=` + `}},cP=Mr.whereImpl,hP=1e-7,dP=1e-4,vm={};function pP(e){return e in vm||(vm[e]={}),vm[e]}var fP=128,mP=600;function AP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*mP/1024/1024}var gl=class extends jl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Or(J().getNumber("WEBGL_VERSION"));this.binaryCache=pP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Hd(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Yz(this.gpgpu),this.numMBBeforeWarning=AP(),this.texData=new eh(this,Sr())}nextDataId(){return gl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Gn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Gn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new yl(i,jd):h=new Ra(i,jd);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=C.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(m=>p.push(m))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new yl(r,jd):p=new Ra(r,jd);let m=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Gu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=p[0],f=p[1];c=C.mergeRealAndImagArrays(m,f)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Sr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Sr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=fP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Sr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new uP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xz(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[si(e.shape),...ii(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[si(t),...ii(t)],s=new Xw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Ld(r),i;n?i=new SD(s):i=new ND(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Hu.DENSE){let f=Gu(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Uu(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=yz(e,l,u),h=this.getAndSaveBinary(c,()=>mz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Az(this.gpgpu,h,l,u,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=v.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(_e(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hP:dP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=mw(n,o),t.texShape=c),a!=null){let h=Ld(n),d,p=c[1],m=c[0],f=a instanceof Uint8Array;o?([p,m]=dl(c[0],c[1]),d=new RD(h,[m,p],f)):d=new CD(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Gn.PIXELS:this.texData.get(A.dataId).usage=Gn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=yP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};gl.nextDataId=0;function yP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew gl,2);var gP={forceHalfFloat:e_},t_=` if (isnan(a)) return a; if (isnan(b)) return b; -`,gl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` +`,xl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -952,12 +952,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},Hd=` + `}},Gd=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`,qu=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` +`,Xu=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Fn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var fP={kernelName:hs,backendName:"webgl",kernelFunc:Fn};function Ra(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Fn({inputs:{x:r},backend:n}),l=Fn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var mP={kernelName:lh,backendName:"webgl",kernelFunc:Ra},Yw="return (a < 0.) ? b * a : a;",Jw=` + `}};function Fn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var xP={kernelName:ds,backendName:"webgl",kernelFunc:Fn};function Fa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Fn({inputs:{x:r},backend:n}),l=Fn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var wP={kernelName:uh,backendName:"webgl",kernelFunc:Fa},n_="return (a < 0.) ? b * a : a;",r_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function AP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(Jw,a.shape,i.shape):new gl(Yw,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var yP={kernelName:ds,backendName:"webgl",kernelFunc:AP},Qw="return (a < 0.) ? b * a : a;",e_=` +`;function _P(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(r_,a.shape,i.shape):new xl(n_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var bP={kernelName:ps,backendName:"webgl",kernelFunc:_P},a_="return (a < 0.) ? b * a : a;",s_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function gP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(e_,r.shape,a.shape):new gl(Qw,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var xP={kernelName:ks,backendName:"webgl",kernelFunc:gP},t_="if (isnan(x)) return x;",wP=` +`;function vP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(s_,r.shape,a.shape):new xl(a_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var kP={kernelName:Is,backendName:"webgl",kernelFunc:vP},i_="if (isnan(x)) return x;",IP=` if (isnan(a)) return a; if (isnan(b)) return b; -`,_P=` +`,NP=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`;function He({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Al(i.shape,t):c=new Ca(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},T=new gl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Yn(b.dtype,w.dtype))}),g=Ra({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Yn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,m.values,f.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new qu(t,l.shape,u.shape,n):p=new gl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Gd(e,t=!1){if(e==="linear")return t?eP:Zz;if(e==="relu")return t?nP:Jz;if(e==="elu")return t?tP:Yz;if(e==="relu6")return t?rP:Qz;if(e==="prelu")return t?e_:Qw;if(e==="leakyrelu")return t?Jw:Yw;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var n_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) { +`;function Ge({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new yl(i.shape,t):c=new Ra(i.shape,e),o.runWebGLProgram(c,[i],l)}}function Qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},T=new xl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],Qn(b.dtype,w.dtype))}),g=Fa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||Qn(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,m.values,f.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new Xu(t,l.shape,u.shape,n):p=new xl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function qd(e,t=!1){if(e==="linear")return t?sP:tP;if(e==="relu")return t?oP:rP;if(e==="elu")return t?iP:nP;if(e==="relu6")return t?lP:aP;if(e==="prelu")return t?s_:a_;if(e==="leakyrelu")return t?r_:n_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},r_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},a_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` + `}},l_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},u_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},s_="return a * b;";function i_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new a_(r_.REAL,r.shape,a.shape),c=new a_(r_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Ra({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Tz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new qu(s_,r.shape,a.shape):i=new gl(s_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var bP={kernelName:ws,backendName:"webgl",kernelFunc:i_};function vP(e,t,n){let r=[ai(e.shape),...si(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[ai(t),...si(t)],i=new Uw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!Vu(a.shape,l)&&!(c.texture!==null&&Vu(c.shape,l))?vP(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var kP={kernelName:_o,backendName:"webgl",kernelFunc:Ae},o_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` + `}},c_="return a * b;";function h_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new u_(l_.REAL,r.shape,a.shape),c=new u_(l_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Fa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=Mz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Xu(c_,r.shape,a.shape):i=new xl(c_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var SP={kernelName:_s,backendName:"webgl",kernelFunc:h_};function TP(e,t,n){let r=[si(e.shape),...ii(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[si(t),...ii(t)],i=new Xw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!Uu(a.shape,l)&&!(c.texture!==null&&Uu(c.shape,l))?TP(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var EP={kernelName:bo,backendName:"webgl",kernelFunc:Ae},d_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=` if (inIdx < 0 || inIdx >= ${a}) { return 0.0; } @@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},IP=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` + `}},CP=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function NP(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ui(e,t,n,r){let a=NP(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=it(this.rank),a=Vw("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=it(this.rank),a=qw("rc",this.rank),s=new Array(this.rank);for(let u=0;u=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,m]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],x=r?[g,m,d]:[g,d,m],N=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),T=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],M=Math.max(y,g),D=n?N.shape[1]:N.shape[2],L=s!=null,W=i!=null,U=l==="leakyrelu",j=l!=null?Gd(l,!0):null,X=L||W||U||j!=null,G;if((p===1||m===1)&&D>l_&&X===!1){let Y=N,ae=T;n&&(Y=xn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=xn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=m!==1,ie=m===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,D,1]}}),E.push(Q));let ce=m===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,D]}}),E.push(oe));let pe=i_({inputs:{a:Q,b:oe},backend:a});G=vm({inputs:{x:pe},backend:a,attrs:{axis:ce,keepDims:!0}}),E.push(pe)}else{let Y=Yn(e.dtype,t.dtype),ae=new n_(w,x,[M,p,m],n,r,L,j,W,U),te=[N,T];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function MP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Xd({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var $P={kernelName:Ws,backendName:"webgl",kernelFunc:MP},u_="return abs(x);";function OP(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Bw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Al(r.shape,u_):a=new Ca(r.shape,u_),n.runWebGLProgram(a,[r],r.dtype)}var DP={kernelName:Di,backendName:"webgl",kernelFunc:OP},zP=dr+` + `}};function Xd(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $P(e.shape,t):new MP(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function OP(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=Xd(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let m=v.sizeFromShape(d),f=v.sizeFromShape(e.shape)/m,A=Ae({inputs:{x:c},attrs:{shape:[f,m]},backend:r}),y=Lh(e.dtype),g=ci(A,y,"sum",r),_=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),_}function km(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return OP(a,s,i,n)}var DP={kernelName:Os,backendName:"webgl",kernelFunc:km};function xn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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pe=h_({inputs:{a:Q,b:oe},backend:a});G=km({inputs:{x:pe},backend:a,attrs:{axis:ce,keepDims:!0}}),E.push(pe)}else{let Y=Qn(e.dtype,t.dtype),ae=new o_(w,x,[M,p,m],n,r,L,H,W,U),te=[N,T];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function PP(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Kd({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var LP={kernelName:Bs,backendName:"webgl",kernelFunc:PP},f_="return abs(x);";function WP(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Gw(s.values);return 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r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` + `}},YP=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back vec4 result = ${r}; setOutput(result); } - `}};function Kd(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Fn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Kd({inputs:r.slice(0,o),backend:n}),u=Kd({inputs:r.slice(o),backend:n});return Kd({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Yn(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new GP(r[0].shape,s):new HP(r[0].shape,s);return 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ZP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ui(f,f.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var YP={kernelName:ah,backendName:"webgl",kernelFunc:ZP},JP=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + 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g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var eL={kernelName:ah,backendName:"webgl",kernelFunc:QP};function tL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=a;c!=null&&(h=xn({inputs:{x:a},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(p),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=ci(f,f.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var nL={kernelName:sh,backendName:"webgl",kernelFunc:tL},rL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1258,7 +1258,7 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back } setOutput(float(bestIndex)); } - `}},QP=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=it(o),u=ln("coords",o),c,h;if(s===1){h=o+1;let N=it(h);c=` + `}},aL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=it(o),u=ln("coords",o),c,h;if(s===1){h=o+1;let N=it(h);c=` ${N} sourceLocR = ${N}(${u.join()}, 0); ++${u[o-1]}; ${N} sourceLocG = ${N}(${u.join()}, 0); @@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,BP=He({opSnippet:WP}),VP={kernelName:Pi,back } setOutput(bestIndex); } - `}};function h_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new JP(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=h_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function d_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new QP(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=d_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function p_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=h_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return d_(e,t,r)}function eL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=p_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var tL={kernelName:Ka,backendName:"webgl",kernelFunc:eL};function nL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=p_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var rL={kernelName:ql,backendName:"webgl",kernelFunc:nL},aL=dr+` + `}};function A_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new rL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=A_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function y_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new aL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=y_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function g_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=A_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return y_(e,t,r)}function sL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=g_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var iL={kernelName:Za,backendName:"webgl",kernelFunc:sL};function oL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xn({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=g_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var lL={kernelName:Xl,backendName:"webgl",kernelFunc:oL},uL=fr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,sL=He({opSnippet:aL}),iL={kernelName:Li,backendName:"webgl",kernelFunc:sL},oL=dr+"return log(x + sqrt(x * x + 1.0));",lL=He({opSnippet:oL}),uL={kernelName:Wi,backendName:"webgl",kernelFunc:lL},cL=dr+` +`,cL=Ge({opSnippet:uL}),hL={kernelName:Wi,backendName:"webgl",kernelFunc:cL},dL=fr+"return log(x + sqrt(x * x + 1.0));",pL=Ge({opSnippet:dL}),fL={kernelName:Bi,backendName:"webgl",kernelFunc:pL},mL=fr+` return atan(x); 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s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vL=Ge({opSnippet:bL}),kL={kernelName:Ui,backendName:"webgl",kernelFunc:vL},Ku=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); @@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(${_}); } - `}},km=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` + `}},Im=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${y}); @@ -1640,7 +1640,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(${w}); } } - `}};function wL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Xu(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var _L={kernelName:Za,backendName:"webgl",kernelFunc:wL};function bL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new km(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var vL={kernelName:Xl,backendName:"webgl",kernelFunc:bL},kL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` + `}};function IL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Ku(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var NL={kernelName:Ya,backendName:"webgl",kernelFunc:IL};function SL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new Im(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var TL={kernelName:Kl,backendName:"webgl",kernelFunc:SL},EL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${h}); @@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},IL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` + `}},CL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); const float avgMultiplier = float(${A}); @@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function NL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new IL(d);return n.runWebGLProgram(p,[a],i.dtype)}var SL={kernelName:ih,backendName:"webgl",kernelFunc:NL};function TL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;cl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new kL(c);return n.runWebGLProgram(h,[a],i.dtype)}var EL={kernelName:sh,backendName:"webgl",kernelFunc:TL};function CL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Xd({a,b:s,transposeA:i,transposeB:o,backend:n})}var RL={kernelName:Ya,backendName:"webgl",kernelFunc:CL},FL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function RL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new CL(d);return n.runWebGLProgram(p,[a],i.dtype)}var FL={kernelName:oh,backendName:"webgl",kernelFunc:RL};function ML(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;hl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new EL(c);return n.runWebGLProgram(h,[a],i.dtype)}var $L={kernelName:ih,backendName:"webgl",kernelFunc:ML};function OL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Kd({a,b:s,transposeA:i,transposeB:o,backend:n})}var DL={kernelName:Ja,backendName:"webgl",kernelFunc:OL},zL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},ML=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},PL=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput((x - mean) * inv + offset); } - `}},$L=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new ML(r.shape,a.shape,s.shape,c,h,l):new FL(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},OL={kernelName:us,backendName:"webgl",kernelFunc:$L},zL=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=`uniform int start[${this.rank}];`,r=DL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Im[o]} = start[${o}] + coords.${Im[o]};`);a=` + `}},LL=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new PL(r.shape,a.shape,s.shape,c,h,l):new zL(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},WL={kernelName:cs,backendName:"webgl",kernelFunc:LL},VL=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=`uniform int start[${this.rank}];`,r=BL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Nm[o]} = start[${o}] + coords.${Nm[o]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` @@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${a} setOutput(getSource(${r})); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Im=["x","y","z","w","u","v"];function DL(e){if(e===1)return"sourceLoc";if(e<=6)return Im.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var PL=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=ln("coords",this.rank),r=ln("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Nm=["x","y","z","w","u","v"];function BL(e){if(e===1)return"sourceLoc";if(e<=6)return Nm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var UL=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=it(this.rank),n=ln("coords",this.rank),r=ln("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; @@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${o} setOutput(result); } - `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function LL(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=an.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ku(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=an.parseSliceParams(a,s,i);if(an.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Mz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=an.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PL(l):new zL(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),LL(a,o,l,n)}var WL={kernelName:Io,backendName:"webgl",kernelFunc:Ku},BL=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],m=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:c}}),y=Ku({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},VL={kernelName:Kl,backendName:"webgl",kernelFunc:BL};function UL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Ww(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var jL={kernelName:oh,backendName:"webgl",kernelFunc:UL},HL="return float(a != b);",f_=Qt({opSnippet:HL,dtype:"bool"}),GL={kernelName:po,backendName:"webgl",kernelFunc:f_};function Zu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.real},backend:n})}var qL={kernelName:Th,backendName:"webgl",kernelFunc:Zu},XL="return float(int(x));";function KL(e,t){let n=new Ca(e.shape,XL),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Nm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Fn({inputs:{x:a},backend:n});let i=Nt(a.shape),o=Nm({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ra({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Zu({inputs:{input:a},backend:n}),o=Nm({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Fn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return KL(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=f_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var ZL={kernelName:Ja,backendName:"webgl",kernelFunc:Nm},m_="return ceil(x);",YL=He({opSnippet:m_,packedOpSnippet:m_,cpuKernelImpl:mz}),JL={kernelName:Qa,backendName:"webgl",kernelFunc:YL},QL=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function HL(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=an.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Zu(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=an.parseSliceParams(a,s,i);if(an.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=Pz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=an.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UL(l):new VL(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),HL(a,o,l,n)}var jL={kernelName:No,backendName:"webgl",kernelFunc:Zu},GL=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=[],m=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:c}}),y=Zu({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},qL={kernelName:Zl,backendName:"webgl",kernelFunc:GL};function XL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=jw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var KL={kernelName:lh,backendName:"webgl",kernelFunc:XL},ZL="return float(a != b);",x_=Qt({opSnippet:ZL,dtype:"bool"}),YL={kernelName:fo,backendName:"webgl",kernelFunc:x_};function Yu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.real},backend:n})}var JL={kernelName:Eh,backendName:"webgl",kernelFunc:Yu},QL="return float(int(x));";function eW(e,t){let n=new Ra(e.shape,QL),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Sm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Fn({inputs:{x:a},backend:n});let i=St(a.shape),o=Sm({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Fa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Yu({inputs:{input:a},backend:n}),o=Sm({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Fn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return eW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=x_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var tW={kernelName:Qa,backendName:"webgl",kernelFunc:Sm},w_="return ceil(x);",nW=Ge({opSnippet:w_,packedOpSnippet:w_,cpuKernelImpl:wz}),rW={kernelName:es,backendName:"webgl",kernelFunc:nW},aW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},eW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},sW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; @@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function tW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new eW(a.shape):o=new QL(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var nW={kernelName:Aa,backendName:"webgl",kernelFunc:tW},rW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function iW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new sW(a.shape):o=new aW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var oW={kernelName:ya,backendName:"webgl",kernelFunc:iW},lW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function A_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function aW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new rW(r.shape),i=[A_(r,a.complexTensorInfos.real),A_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var sW={kernelName:Zl,backendName:"webgl",kernelFunc:aW},iW=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( - getT${m}(${Zd(i,l,f)}), - vec2(${Zd(u,l,f)})); + getT${m}(${Yd(i,l,f)}), + vec2(${Yd(u,l,f)})); }`}let d=o.length,p=o[o.length-1];h+=` return getChannel( - getT${d}(${Zd(i,l,p)}), - vec2(${Zd(u,l,p)}));`,this.userCode=` + getT${d}(${Yd(i,l,p)}), + vec2(${Yd(u,l,p)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${h} } @@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(result); } - `}};function Zd(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Yd(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var lW={kernelName:_h,backendName:"webgl",kernelFunc:Yd};function xl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>Zu({inputs:{input:m},backend:n})),c=e.map(m=>Yd({inputs:{input:m},backend:n})),h=xl(u,t,n),d=xl(c,t,n),p=Ra({inputs:{real:h,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=y_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=Az(h,c,r,d),m=C.computeOutShape(e.map(A=>A.shape),t),f=n.makeTensorInfo(m,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),f}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=xl(e.slice(0,u),t,n),h=xl(e.slice(u),t,n),d=xl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new oW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=y_(e,t,n),i=new iW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function y_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function g_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Fn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),xl(o,s,n)}var uW={kernelName:ji,backendName:"webgl",kernelFunc:g_},x_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,_="",b="";n&&(r?_=`float activation(float a) { + `}};function Yd(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Jd(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var pW={kernelName:bh,backendName:"webgl",kernelFunc:Jd};function wl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>Yu({inputs:{input:m},backend:n})),c=e.map(m=>Jd({inputs:{input:m},backend:n})),h=wl(u,t,n),d=wl(c,t,n),p=Fa({inputs:{real:h,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=b_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=_z(h,c,r,d),m=C.computeOutShape(e.map(A=>A.shape),t),f=n.makeTensorInfo(m,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),f}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=wl(e.slice(0,u),t,n),h=wl(e.slice(u),t,n),d=wl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new dW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=b_(e,t,n),i=new hW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function b_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function v_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Fn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),wl(o,s,n)}var fW={kernelName:ji,backendName:"webgl",kernelFunc:v_},k_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,_="",b="";n&&(r?_=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?_=`float activation(float a) { @@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${b} setOutput(result); } - `}},cW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},mW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${a}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${r}); @@ -2110,7 +2110,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},hW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=on(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=` + `}},AW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=on(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=` blockIndex = rc.y + ${w}; pos = rc.x + ${b}; @@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${f.output} = result; } - `}};function w_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&c>l_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Xd({a:w,b:x,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Vu(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Xd({a:w,b:N,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Fn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function __({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*u*c,A=d*h,y=[f,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(x);let N=new hW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=Ae({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",W=o?Gd(o,!0):null,U=new n_(E.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,L),j=[E,x];if(a&&j.push(a),D&&j.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function dW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=w_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=__({x:a,filter:s,convInfo:d,backend:n});else{let f=new x_(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var pW={kernelName:es,backendName:"webgl",kernelFunc:dW},fW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function I_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&c>p_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Kd({a:w,b:x,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Uu(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Kd({a:w,b:N,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Fn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function N_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*u*c,A=d*h,y=[f,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(x);let N=new AW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=Ae({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",W=o?qd(o,!0):null,U=new o_(E.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,L),H=[E,x];if(a&&H.push(a),D&&H.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function yW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=I_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=N_({x:a,filter:s,convInfo:d,backend:n});else{let f=new k_(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var gW={kernelName:ts,backendName:"webgl",kernelFunc:yW},xW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},mW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` + `}},wW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},AW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},_W=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},yW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` + `}},bW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function gW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new fW(d);return n.runWebGLProgram(p,[a,s],"float32")}var xW={kernelName:uh,backendName:"webgl",kernelFunc:gW};function wW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new mW(d);return n.runWebGLProgram(p,[a,s],"float32")}var _W={kernelName:ts,backendName:"webgl",kernelFunc:wW};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new cW(u);return n.runWebGLProgram(c,[a,s],"float32")}var vW={kernelName:Yl,backendName:"webgl",kernelFunc:bW};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new AW(u);return n.runWebGLProgram(c,[a,s],"float32")}var IW={kernelName:ch,backendName:"webgl",kernelFunc:kW};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new yW(u);return n.runWebGLProgram(c,[a,s],"float32")}var SW={kernelName:hh,backendName:"webgl",kernelFunc:NW},TW=t_+` + `}};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new xW(d);return n.runWebGLProgram(p,[a,s],"float32")}var kW={kernelName:ch,backendName:"webgl",kernelFunc:vW};function IW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new wW(d);return n.runWebGLProgram(p,[a,s],"float32")}var NW={kernelName:ns,backendName:"webgl",kernelFunc:IW};function SW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new mW(u);return n.runWebGLProgram(c,[a,s],"float32")}var TW={kernelName:Jl,backendName:"webgl",kernelFunc:SW};function EW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new _W(u);return n.runWebGLProgram(c,[a,s],"float32")}var CW={kernelName:hh,backendName:"webgl",kernelFunc:EW};function RW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new bW(u);return n.runWebGLProgram(c,[a,s],"float32")}var FW={kernelName:dh,backendName:"webgl",kernelFunc:RW},MW=i_+` return cos(x); -`,EW=He({opSnippet:TW}),CW={kernelName:ns,backendName:"webgl",kernelFunc:EW},RW=` +`,$W=Ge({opSnippet:MW}),OW={kernelName:rs,backendName:"webgl",kernelFunc:$W},DW=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,FW=He({opSnippet:RW}),MW={kernelName:Hi,backendName:"webgl",kernelFunc:FW},$W=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,zW=Ge({opSnippet:DW}),PW={kernelName:Gi,backendName:"webgl",kernelFunc:zW},LW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${g}); void main() { @@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(newValue); } } - `}},OW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new $W(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},DW={kernelName:Gi,backendName:"webgl",kernelFunc:OW},k_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${b_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` + `}},WW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new LW(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},BW={kernelName:qi,backendName:"webgl",kernelFunc:WW},E_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${S_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${it(r)} coords = getOutputCoords(); - int end = ${v_(r,"coords")}; + int end = ${T_(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; - ${v_(r,"coords")} = idx; - val += getX(${b_(r,"coords")}); + ${T_(r,"coords")} = idx; + val += getX(${S_(r,"coords")}); } setOutput(val); } - `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function b_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function zW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=xn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Fn({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new k_(c.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new k_(c.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=C.getUndoAxesPermutation(u),f=xn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),f}return p}var PW={kernelName:rs,backendName:"webgl",kernelFunc:zW};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Ww(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=fz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var WW={kernelName:dh,backendName:"webgl",kernelFunc:LW},BW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function S_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function T_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function VW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=xn({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Fn({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new E_(c.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new E_(c.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=C.getUndoAxesPermutation(u),f=xn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),f}return p}var UW={kernelName:as,backendName:"webgl",kernelFunc:VW};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=jw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=xz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var jW={kernelName:ph,backendName:"webgl",kernelFunc:HW},GW=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function VW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new BW(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var UW={kernelName:qi,backendName:"webgl",kernelFunc:VW},I_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function qW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new GW(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var XW={kernelName:Xi,backendName:"webgl",kernelFunc:qW},C_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { @@ -2503,7 +2503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${y} setOutput(result); } - `}},N_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=m,A="int xR; int xC; int xCOffset;";for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new N_(h):d=new I_(h),n.runWebGLProgram(d,[a,s],"float32")}var HW={kernelName:as,backendName:"webgl",kernelFunc:jW},GW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function KW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new R_(h):d=new C_(h),n.runWebGLProgram(d,[a,s],"float32")}var ZW={kernelName:ss,backendName:"webgl",kernelFunc:KW},YW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},qW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},JW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -2736,13 +2736,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function XW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new GW(h);return n.runWebGLProgram(d,[a,s],"float32")}var KW={kernelName:ph,backendName:"webgl",kernelFunc:XW};function ZW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new qW(h);return n.runWebGLProgram(d,[a,s],"float32")}var YW={kernelName:fh,backendName:"webgl",kernelFunc:ZW},JW=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function QW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new YW(h);return n.runWebGLProgram(d,[a,s],"float32")}var eB={kernelName:fh,backendName:"webgl",kernelFunc:QW};function tB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new JW(h);return n.runWebGLProgram(d,[a,s],"float32")}var nB={kernelName:mh,backendName:"webgl",kernelFunc:tB},rB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function QW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new JW(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var eB={kernelName:mh,backendName:"webgl",kernelFunc:QW},tB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` + `}};function aB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new rB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var sB={kernelName:Ah,backendName:"webgl",kernelFunc:aB},iB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${c}, ${h}); const float neg_infinity = -3.4e38; @@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float result = curVal; setOutput(result); } - `}};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new tB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var rB={kernelName:Jl,backendName:"webgl",kernelFunc:nB},aB="return (x >= 0.0) ? x : (exp(x) - 1.0);",sB=` + `}};function oB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new iB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var lB={kernelName:Ql,backendName:"webgl",kernelFunc:oB},uB="return (x >= 0.0) ? x : (exp(x) - 1.0);",cB=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,iB=He({opSnippet:aB,packedOpSnippet:sB}),oB={kernelName:Xi,backendName:"webgl",kernelFunc:iB},lB="return (b >= 1.0) ? a : a * (b + 1.0);",uB=` +`,hB=Ge({opSnippet:uB,packedOpSnippet:cB}),dB={kernelName:Ki,backendName:"webgl",kernelFunc:hB},pB="return (b >= 1.0) ? a : a * (b + 1.0);",fB=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,cB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qu(uB,r.shape,a.shape):new gl(lB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},hB={kernelName:gh,backendName:"webgl",kernelFunc:cB},dB=` +`,mB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xu(fB,r.shape,a.shape):new xl(pB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},AB={kernelName:xh,backendName:"webgl",kernelFunc:mB},yB=` return vec4(equal(a, b)); -`,pB="return float(a == b);",fB=Qt({opSnippet:pB,packedOpSnippet:dB,dtype:"bool"}),mB={kernelName:Zi,backendName:"webgl",kernelFunc:fB},AB=` +`,gB="return float(a == b);",xB=Qt({opSnippet:gB,packedOpSnippet:yB,dtype:"bool"}),wB={kernelName:Yi,backendName:"webgl",kernelFunc:xB},_B=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,yB=He({opSnippet:AB}),gB={kernelName:Ki,backendName:"webgl",kernelFunc:yB},S_="return exp(x);",T_=He({opSnippet:S_,packedOpSnippet:S_,cpuKernelImpl:yz}),xB={kernelName:is,backendName:"webgl",kernelFunc:T_};function Sm(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var wB={kernelName:Yi,backendName:"webgl",kernelFunc:Sm},E_="return exp(x) - 1.0;",_B=He({opSnippet:E_,packedOpSnippet:E_,cpuKernelImpl:gz}),bB={kernelName:Ji,backendName:"webgl",kernelFunc:_B},C_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,bB=Ge({opSnippet:_B}),vB={kernelName:Zi,backendName:"webgl",kernelFunc:bB},F_="return exp(x);",M_=Ge({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:bz}),kB={kernelName:os,backendName:"webgl",kernelFunc:M_};function Tm(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var IB={kernelName:Ji,backendName:"webgl",kernelFunc:Tm},$_="return exp(x) - 1.0;",NB=Ge({opSnippet:$_,packedOpSnippet:$_,cpuKernelImpl:vz}),SB={kernelName:Qi,backendName:"webgl",kernelFunc:NB},O_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${a}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function R_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new C_("real",l,t),c=new C_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Ra({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function vB(e){let{inputs:t,backend:n}=e,{input:r}=t;return R_(r,!1,n)}var kB={kernelName:xh,backendName:"webgl",kernelFunc:vB},IB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function D_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new O_("real",l,t),c=new O_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),m=Fa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function TB(e){let{inputs:t,backend:n}=e,{input:r}=t;return D_(r,!1,n)}var EB={kernelName:wh,backendName:"webgl",kernelFunc:TB},CB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } - `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Tm(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new IB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var NB={kernelName:Ql,backendName:"webgl",kernelFunc:Tm},SB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Em(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new CB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var RB={kernelName:eu,backendName:"webgl",kernelFunc:Em},FB=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(outputValue); } - `}},TB={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new SB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},F_="return floor(x);",EB=He({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:xz}),CB={kernelName:os,backendName:"webgl",kernelFunc:EB},RB=` + `}},MB={kernelName:eo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new FB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},z_="return floor(x);",$B=Ge({opSnippet:z_,packedOpSnippet:z_,cpuKernelImpl:kz}),OB={kernelName:ls,backendName:"webgl",kernelFunc:$B},DB=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } else { return NAN; } -`,FB=` +`,zB=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,MB=Qt({opSnippet:RB,packedOpSnippet:FB,dtype:"int32"}),$B={kernelName:ls,backendName:"webgl",kernelFunc:MB},OB=class{constructor(e){this.variableNames=["A"];let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` +`,PB=Qt({opSnippet:DB,packedOpSnippet:zB,dtype:"int32"}),LB={kernelName:us,backendName:"webgl",kernelFunc:PB},WB=class{constructor(e){this.variableNames=["A"];let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},DB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` + `}},BB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=on(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${t.output} = result; } - `}},PB={kernelName:Mh,backendName:"webgl",kernelFunc:zB},wl;function zB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(wl==null&&(wl=document.createElement("canvas").getContext("2d")),wl.canvas.width=u,wl.canvas.height=c,wl.drawImage(a,0,0,u,c),a=wl.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Hn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let m=J().getBool("WEBGL_PACK")?new DB(d):new OB(d),f=n.runWebGLProgram(m,[p],"int32");return n.disposeData(p.dataId),f}function LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=w_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=__({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?Gd(p,!1):null,T=new x_(A,b,N,w,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var WB={kernelName:Bs,backendName:"webgl",kernelFunc:LB};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=c;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?Gd(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(E),m.push(E)}let N;y?N=new N_(A,b,g,w,x):N=new I_(A,b,g,w,x);let T=n.runWebGLProgram(N,_,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var VB={kernelName:Vs,backendName:"webgl",kernelFunc:BB},UB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=it(t.length),a=it(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` + `}},UB={kernelName:$h,backendName:"webgl",kernelFunc:VB},_l;function VB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(_l==null&&(_l=document.createElement("canvas").getContext("2d")),_l.canvas.width=u,_l.canvas.height=c,_l.drawImage(a,0,0,u,c),a=_l.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Gn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let m=J().getBool("WEBGL_PACK")?new BB(d):new WB(d),f=n.runWebGLProgram(m,[p],"int32");return n.disposeData(p.dataId),f}function HB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=I_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=N_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?qd(p,!1):null,T=new k_(A,b,N,w,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var jB={kernelName:Vs,backendName:"webgl",kernelFunc:HB};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=c;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?qd(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(E),m.push(E)}let N;y?N=new R_(A,b,g,w,x):N=new C_(A,b,g,w,x);let T=n.runWebGLProgram(N,_,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var qB={kernelName:Us,backendName:"webgl",kernelFunc:GB},XB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=it(t.length),a=it(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${r} strides = ${r}(${this.strides}); void main() { ${a} coords = getOutputCoords(); @@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(getX(flattenIndex, coords[1])); } - `}};function jB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new UB(i,c,[l,u]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var HB={kernelName:to,backendName:"webgl",kernelFunc:jB},qB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=it(this.rank),r=GB(e,2);this.userCode=` + `}};function KB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new XB(i,c,[l,u]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var ZB={kernelName:no,backendName:"webgl",kernelFunc:KB},JB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=it(this.rank),r=YB(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${r})); } - `}};function GB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new qB(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var KB={kernelName:eo,backendName:"webgl",kernelFunc:XB},ZB="return float(a > b);",YB=` + `}};function YB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new JB(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var eV={kernelName:to,backendName:"webgl",kernelFunc:QB},tV="return float(a > b);",nV=` return vec4(greaterThan(a, b)); -`,JB=Qt({opSnippet:ZB,packedOpSnippet:YB,cpuKernelImpl:_z,dtype:"bool"}),QB={kernelName:no,backendName:"webgl",kernelFunc:JB},eV="return float(a >= b);",tV=` +`,rV=Qt({opSnippet:tV,packedOpSnippet:nV,cpuKernelImpl:Nz,dtype:"bool"}),aV={kernelName:ro,backendName:"webgl",kernelFunc:rV},sV="return float(a >= b);",iV=` return vec4(greaterThanEqual(a, b)); -`,nV=Qt({opSnippet:eV,packedOpSnippet:tV,dtype:"bool"}),rV={kernelName:cs,backendName:"webgl",kernelFunc:nV};function aV(e){let{inputs:t,backend:n}=e,{input:r}=t;return R_(r,!0,n)}var sV={kernelName:wh,backendName:"webgl",kernelFunc:aV},iV="return float(!isnan(x) && !isinf(x));",oV=He({opSnippet:iV,dtype:"bool"}),lV={kernelName:ro,backendName:"webgl",kernelFunc:oV},uV="return float(isinf(x));",cV=He({opSnippet:uV,dtype:"bool"}),hV={kernelName:ao,backendName:"webgl",kernelFunc:cV},dV="return float(isnan(x));",pV=He({opSnippet:dV,dtype:"bool"}),fV={kernelName:so,backendName:"webgl",kernelFunc:pV},mV="return float(a < b);",AV=` +`,oV=Qt({opSnippet:sV,packedOpSnippet:iV,dtype:"bool"}),lV={kernelName:hs,backendName:"webgl",kernelFunc:oV};function uV(e){let{inputs:t,backend:n}=e,{input:r}=t;return D_(r,!0,n)}var cV={kernelName:_h,backendName:"webgl",kernelFunc:uV},hV="return float(!isnan(x) && !isinf(x));",dV=Ge({opSnippet:hV,dtype:"bool"}),pV={kernelName:ao,backendName:"webgl",kernelFunc:dV},fV="return float(isinf(x));",mV=Ge({opSnippet:fV,dtype:"bool"}),AV={kernelName:so,backendName:"webgl",kernelFunc:mV},yV="return float(isnan(x));",gV=Ge({opSnippet:yV,dtype:"bool"}),xV={kernelName:io,backendName:"webgl",kernelFunc:gV},wV="return float(a < b);",_V=` return vec4(lessThan(a, b)); -`,yV=Qt({opSnippet:mV,packedOpSnippet:AV,cpuKernelImpl:bz,dtype:"bool"}),gV={kernelName:io,backendName:"webgl",kernelFunc:yV},xV="return float(a <= b);",wV=` +`,bV=Qt({opSnippet:wV,packedOpSnippet:_V,cpuKernelImpl:Sz,dtype:"bool"}),vV={kernelName:oo,backendName:"webgl",kernelFunc:bV},kV="return float(a <= b);",IV=` return vec4(lessThanEqual(a, b)); -`,_V=Qt({opSnippet:xV,packedOpSnippet:wV,dtype:"bool"}),bV={kernelName:oo,backendName:"webgl",kernelFunc:_V};function vV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=vz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var kV={kernelName:bh,backendName:"webgl",kernelFunc:vV},IV=`if (x < 0.0) return NAN; - return log(x);`,NV=` +`,NV=Qt({opSnippet:kV,packedOpSnippet:IV,dtype:"bool"}),SV={kernelName:lo,backendName:"webgl",kernelFunc:NV};function TV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Tz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var EV={kernelName:vh,backendName:"webgl",kernelFunc:TV},CV=`if (x < 0.0) return NAN; + return log(x);`,RV=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; @@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam result.a = isNaN.a == 1.0 ? NAN : result.a; return result; -`,SV=He({opSnippet:IV,packedOpSnippet:NV,cpuKernelImpl:kz}),TV={kernelName:ps,backendName:"webgl",kernelFunc:SV},EV="return log(1.0 + x);",CV=He({opSnippet:EV}),RV={kernelName:lo,backendName:"webgl",kernelFunc:CV},FV="return float(a >= 1.0 && b >= 1.0);",MV=` +`,FV=Ge({opSnippet:CV,packedOpSnippet:RV,cpuKernelImpl:Ez}),MV={kernelName:fs,backendName:"webgl",kernelFunc:FV},$V="return log(1.0 + x);",OV=Ge({opSnippet:$V}),DV={kernelName:uo,backendName:"webgl",kernelFunc:OV},zV="return float(a >= 1.0 && b >= 1.0);",PV=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,$V=Qt({opSnippet:FV,packedOpSnippet:MV,dtype:"bool"}),OV={kernelName:uo,backendName:"webgl",kernelFunc:$V},DV="return float(!(x >= 1.0));",zV=He({opSnippet:DV}),PV={kernelName:eu,backendName:"webgl",kernelFunc:zV},LV="return float(a >= 1.0 || b >= 1.0);",WV=` +`,LV=Qt({opSnippet:zV,packedOpSnippet:PV,dtype:"bool"}),WV={kernelName:co,backendName:"webgl",kernelFunc:LV},BV="return float(!(x >= 1.0));",VV=Ge({opSnippet:BV}),UV={kernelName:tu,backendName:"webgl",kernelFunc:VV},HV="return float(a >= 1.0 || b >= 1.0);",jV=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,BV=Qt({opSnippet:LV,packedOpSnippet:WV,dtype:"bool"}),VV={kernelName:tu,backendName:"webgl",kernelFunc:BV},UV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` +`,GV=Qt({opSnippet:HV,packedOpSnippet:jV,dtype:"bool"}),qV={kernelName:nu,backendName:"webgl",kernelFunc:GV},XV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam float val = x * ${o}; setOutput(val); } - `}},jV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` + `}},KV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},HV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new jV(a.shape,s,i,o,l):new UV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},GV={kernelName:nu,backendName:"webgl",kernelFunc:HV},qV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` + `}},ZV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new KV(a.shape,s,i,o,l):new XV(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},YV={kernelName:ru,backendName:"webgl",kernelFunc:ZV},JV=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(result); } - `}},XV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new qV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},KV={kernelName:vh,backendName:"webgl",kernelFunc:XV};function ZV(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ui(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function M_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=_m(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=qd(a,c,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=Iz(g,v.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=ZV(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var YV={kernelName:fs,backendName:"webgl",kernelFunc:M_},JV=Zw+` + `}},QV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new JV(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},eU={kernelName:kh,backendName:"webgl",kernelFunc:QV};function tU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function P_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=bm(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=Xd(a,c,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=Cz(g,v.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=tU(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var nU={kernelName:ms,backendName:"webgl",kernelFunc:P_},rU=t_+` return max(a, b); -`,QV=` +`,aU=` vec4 result = vec4(max(a, b)); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); - `+Hd+` + `+Gd+` return result; -`,eU=Qt({opSnippet:JV,packedOpSnippet:QV,cpuKernelImpl:Nz}),tU={kernelName:ms,backendName:"webgl",kernelFunc:eU};function nU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Xu(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var rU={kernelName:As,backendName:"webgl",kernelFunc:nU};function aU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new km(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var sU={kernelName:ru,backendName:"webgl",kernelFunc:aU},iU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` +`,sU=Qt({opSnippet:rU,packedOpSnippet:aU,cpuKernelImpl:Rz}),iU={kernelName:As,backendName:"webgl",kernelFunc:sU};function oU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Fn({inputs:{x:a},backend:n});let h=new Ku(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var lU={kernelName:ys,backendName:"webgl",kernelFunc:oU};function uU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new Im(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var cU={kernelName:au,backendName:"webgl",kernelFunc:uU},hU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}},oU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` + `}},dU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${h}, ${d}); void main() { @@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam } setOutput(dotProd); } - `}};function lU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new km(d,"max",!0),m=n.runWebGLProgram(p,[i],i.dtype),f=new oU(d),A=n.runWebGLProgram(f,[a,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var uU={kernelName:Ih,backendName:"webgl",kernelFunc:lU};function cU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;cl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,m=new Xu(d,"max",p),f=n.runWebGLProgram(m,[o],o.dtype),A=new iU(d),y=n.runWebGLProgram(A,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var hU={kernelName:kh,backendName:"webgl",kernelFunc:cU};function dU(e,t,n,r){let a=new Xu(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new Xu(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var pU={kernelName:Nh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,a,s,u,i),[h,d]=dU(r,o,c,l);return[h,d]}};function fU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ui(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var mU={kernelName:ys,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let _=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;N{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,a,s,u,i),[h,d]=yU(r,o,c,l);return[h,d]}};function xU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var wU={kernelName:gs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let _=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;Nu[0]+e[c]+u[1]);let r=e.length,a=it(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=` +`,IU=Qt({opSnippet:vU,packedOpSnippet:kU,cpuKernelImpl:Fz}),NU={kernelName:ws,backendName:"webgl",kernelFunc:IU},SU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=it(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${a} coords = outC - start; setOutput(getX(${o})); } - `}},vU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=it(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` + `}},TU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=it(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` ${a} source = rc; if (source < start) { source = start * 2 - source - ${h}; @@ -3337,13 +3337,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam ${d} setOutput(result); } - `}},kU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vU(r.shape,a,s):new bU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},IU={kernelName:au,backendName:"webgl",kernelFunc:kU},NU=`if (b == 0.0) return NAN; - return mod(a, b);`,SU=` + `}},EU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TU(r.shape,a,s):new SU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},CU={kernelName:su,backendName:"webgl",kernelFunc:EU},RU=`if (b == 0.0) return NAN; + return mod(a, b);`,FU=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); - `+Hd+` + `+Gd+` return result; -`,TU=Qt({opSnippet:NU,packedOpSnippet:SU}),EU={kernelName:co,backendName:"webgl",kernelFunc:TU},CU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` +`,MU=Qt({opSnippet:RU,packedOpSnippet:FU}),$U={kernelName:ho,backendName:"webgl",kernelFunc:MU},OU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; void main() { @@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,gL=He({opSnippet:yL}),xL={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},RU=` + `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},DU=` if (a == b) { return 1.0; }; -return a / b;`,FU=` +return a / b;`,zU=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3387,14 +3387,14 @@ return a / b;`,FU=` } return result; -`,$_=Qt({opSnippet:RU,packedOpSnippet:FU,checkOutOfBounds:!0}),MU={kernelName:ss,backendName:"webgl",kernelFunc:$_},O_="return a - b;",D_=Qt({opSnippet:O_,packedOpSnippet:O_,supportsComplex:!0,cpuKernelImpl:Oz}),$U={kernelName:zs,backendName:"webgl",kernelFunc:D_};function z_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=M_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=D_({inputs:{a,b:u},backend:n}),h=T_({inputs:{x:c},backend:n}),d=vm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),m=$_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var OU={kernelName:Os,backendName:"webgl",kernelFunc:z_};function DU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:z_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new CU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var zU={kernelName:Sh,backendName:"webgl",kernelFunc:DU},P_="return -x;";function PU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Ez(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Al(r.shape,P_):a=new Ca(r.shape,P_),n.runWebGLProgram(a,[r],r.dtype)}var LU={kernelName:ho,backendName:"webgl",kernelFunc:PU},WU=Rr.nonMaxSuppressionV3Impl;function BU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=WU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var VU={kernelName:fo,backendName:"webgl",kernelFunc:BU},UU=Rr.nonMaxSuppressionV4Impl;function jU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=UU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var HU={kernelName:mo,backendName:"webgl",kernelFunc:jU},GU=Rr.nonMaxSuppressionV5Impl;function qU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=GU(c,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var XU={kernelName:Ao,backendName:"webgl",kernelFunc:qU},KU=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`,L_=Qt({opSnippet:DU,packedOpSnippet:zU,checkOutOfBounds:!0}),PU={kernelName:is,backendName:"webgl",kernelFunc:L_},W_="return a - b;",B_=Qt({opSnippet:W_,packedOpSnippet:W_,supportsComplex:!0,cpuKernelImpl:Wz}),LU={kernelName:Ps,backendName:"webgl",kernelFunc:B_};function V_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=P_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=B_({inputs:{a,b:u},backend:n}),h=M_({inputs:{x:c},backend:n}),d=km({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),m=L_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var WU={kernelName:Ds,backendName:"webgl",kernelFunc:V_};function BU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:V_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new OU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var VU={kernelName:Th,backendName:"webgl",kernelFunc:BU},U_="return -x;";function UU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=$z(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,U_):a=new Ra(r.shape,U_),n.runWebGLProgram(a,[r],r.dtype)}var HU={kernelName:po,backendName:"webgl",kernelFunc:UU},jU=Mr.nonMaxSuppressionV3Impl;function GU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=jU(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var qU={kernelName:mo,backendName:"webgl",kernelFunc:GU},XU=Mr.nonMaxSuppressionV4Impl;function KU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=XU(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var ZU={kernelName:Ao,backendName:"webgl",kernelFunc:KU},YU=Mr.nonMaxSuppressionV5Impl;function JU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=YU(c,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var QU={kernelName:yo,backendName:"webgl",kernelFunc:JU},eH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } - `}},ZU=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new KU(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},YU={kernelName:_s,backendName:"webgl",kernelFunc:ZU};function Jd(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Zu({inputs:{input:r},backend:n}),s=Jd({inputs:{x:a},backend:n}),i=Yd({inputs:{input:r},backend:n}),o=Jd({inputs:{x:i},backend:n}),l=Ra({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Tm({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var JU={kernelName:$o,backendName:"webgl",kernelFunc:Jd};function L_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Zu({inputs:{input:r},backend:n}),s=L_({inputs:{x:a},backend:n}),i=Yd({inputs:{input:r},backend:n}),o=Jd({inputs:{x:i},backend:n}),l=Ra({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Tm({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var QU={kernelName:yo,backendName:"webgl",kernelFunc:L_};function ej(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Sm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Sm({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=g_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var tj={kernelName:go,backendName:"webgl",kernelFunc:ej},nj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=it(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` + `}},tH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new eH(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},nH={kernelName:bs,backendName:"webgl",kernelFunc:tH};function Qd(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Yu({inputs:{input:r},backend:n}),s=Qd({inputs:{x:a},backend:n}),i=Jd({inputs:{input:r},backend:n}),o=Qd({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Em({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var rH={kernelName:Oo,backendName:"webgl",kernelFunc:Qd};function H_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Yu({inputs:{input:r},backend:n}),s=H_({inputs:{x:a},backend:n}),i=Jd({inputs:{input:r},backend:n}),o=Qd({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Em({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var aH={kernelName:go,backendName:"webgl",kernelFunc:H_};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Tm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Tm({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=v_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var iH={kernelName:xo,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=it(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3419,7 +3419,7 @@ return a / b;`,FU=` setOutput(getX(${o})); } } - `}},rj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=it(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; + `}},lH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=it(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=ln("rc",r),l=ln("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${u}) { `,r===1?"":`} rc = outputLoc; @@ -3443,7 +3443,7 @@ return a / b;`,FU=` ${p} setOutput(result); } - `}},W_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rj(a.shape,s,i):new nj(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},aj={kernelName:bs,backendName:"webgl",kernelFunc:W_},sj=` + `}},j_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lH(a.shape,s,i):new oH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},uH={kernelName:vs,backendName:"webgl",kernelFunc:j_},cH=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -3452,7 +3452,7 @@ return a / b;`,FU=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,ij=` +`,hH=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -3466,11 +3466,11 @@ return a / b;`,FU=` result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); - `+Hd+` + `+Gd+` return result; -`,oj=Qt({opSnippet:sj,packedOpSnippet:ij}),lj={kernelName:vs,backendName:"webgl",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=xn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=Cz(d.shape,d.dtype,m,c);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(f),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Ph(a.dtype),_=ui(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:m}}),l.push(y),l.push(_)}if(i){l.push(p);let m=C.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var cj={kernelName:xo,backendName:"webgl",kernelFunc:uj},B_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Rz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},hj={kernelName:su,backendName:"webgl",kernelFunc:B_},dj="return 1.0 / x;",pj=He({opSnippet:dj}),fj={kernelName:wo,backendName:"webgl",kernelFunc:pj},mj=dr+` +`,dH=Qt({opSnippet:cH,packedOpSnippet:hH}),pH={kernelName:ks,backendName:"webgl",kernelFunc:dH};function fH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=xn({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=Oz(d.shape,d.dtype,m,c);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(f),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Lh(a.dtype),_=ci(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:m}}),l.push(y),l.push(_)}if(i){l.push(p);let m=C.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var mH={kernelName:wo,backendName:"webgl",kernelFunc:fH},G_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Dz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},AH={kernelName:iu,backendName:"webgl",kernelFunc:G_},yH="return 1.0 / x;",gH=Ge({opSnippet:yH}),xH={kernelName:_o,backendName:"webgl",kernelFunc:gH},wH=fr+` return (x < 0.0) ? 0.0 : x; -`,Aj=` +`,_H=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3480,9 +3480,9 @@ return a / b;`,FU=` result.a = isNaN.a ? x.a : result.a; return result; -`,yj=He({opSnippet:mj,packedOpSnippet:Aj}),gj={kernelName:Is,backendName:"webgl",kernelFunc:yj},xj=dr+` +`,bH=Ge({opSnippet:wH,packedOpSnippet:_H}),vH={kernelName:Ns,backendName:"webgl",kernelFunc:bH},kH=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,wj=` +`,IH=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3492,7 +3492,7 @@ return a / b;`,FU=` result.a = isNaN.a ? x.a : result.a; return result; -`,_j=He({opSnippet:xj,packedOpSnippet:wj}),bj={kernelName:Ss,backendName:"webgl",kernelFunc:_j},vj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,NH=Ge({opSnippet:kH,packedOpSnippet:IH}),SH={kernelName:Ts,backendName:"webgl",kernelFunc:NH},TH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3525,7 +3525,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}},kj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},EH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, @@ -3602,7 +3602,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}};function Ij(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kj(a.shape,l,u,s,i):new vj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var Nj={kernelName:Ns,backendName:"webgl",kernelFunc:Ij},Sj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new EH(a.shape,l,u,s,i):new TH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var RH={kernelName:Ss,backendName:"webgl",kernelFunc:CH},FH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3683,7 +3683,7 @@ return a / b;`,FU=` setOutput(accumulator); } - `}};function Tj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Sj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Ej={kernelName:Ch,backendName:"webgl",kernelFunc:Tj},Cj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function MH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new FH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var $H={kernelName:Rh,backendName:"webgl",kernelFunc:MH},OH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3705,7 +3705,7 @@ return a / b;`,FU=` setOutput(newValue); } - `}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new Cj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var Fj={kernelName:iu,backendName:"webgl",kernelFunc:Rj},Mj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function DH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new OH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var zH={kernelName:ou,backendName:"webgl",kernelFunc:DH},PH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3775,7 +3775,7 @@ return a / b;`,FU=` setOutput(accumulator); } - `}};function $j(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Mj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Oj={kernelName:Eh,backendName:"webgl",kernelFunc:$j},Dj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function LH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new PH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var WH={kernelName:Ch,backendName:"webgl",kernelFunc:LH},BH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -3785,7 +3785,7 @@ return a / b;`,FU=` ${s} coords = getOutputCoords(); setOutput(getX(${a})); } - `}},zj=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=ln("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=it(n);n===1?this.userCode=` + `}},VH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=ln("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=it(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -3813,7 +3813,7 @@ return a / b;`,FU=` } setOutput(result); } - `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Fn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zj(a.shape,o):new Dj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Lj={kernelName:Ts,backendName:"webgl",kernelFunc:Pj},Wj=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` + `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function UH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Fn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VH(a.shape,o):new BH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var HH={kernelName:Es,backendName:"webgl",kernelFunc:UH},jH=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` vec3 fill = vec3(${n.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -3830,7 +3830,7 @@ return a / b;`,FU=` } setOutput(outputValue); } - `}},Bj={kernelName:Oo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Wj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},Vj=` + `}},GH={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new jH(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},qH=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -3845,7 +3845,7 @@ return a / b;`,FU=` return base + 1.0; } } -`,Uj=He({opSnippet:Vj}),jj={kernelName:Es,backendName:"webgl",kernelFunc:Uj},Hj="return inversesqrt(x);",Gj=He({opSnippet:Hj,cpuKernelImpl:Fz}),qj={kernelName:Cs,backendName:"webgl",kernelFunc:Gj},V_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=it(a.length),l=it(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` +`,XH=Ge({opSnippet:qH}),KH={kernelName:Cs,backendName:"webgl",kernelFunc:XH},ZH="return inversesqrt(x);",YH=Ge({opSnippet:ZH,cpuKernelImpl:zz}),JH={kernelName:Rs,backendName:"webgl",kernelFunc:YH},q_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=it(a.length),l=it(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${a}); void main() { @@ -3865,7 +3865,7 @@ return a / b;`,FU=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function Xj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=C.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new V_(l,o,p.shape.length,m.shape.length,c,d),y=n.runWebGLProgram(A,[m,p,f],m.dtype),g=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var Kj={kernelName:bo,backendName:"webgl",kernelFunc:Xj},Zj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,eH=He({opSnippet:Qj}),tH={kernelName:ko,backendName:"webgl",kernelFunc:eH},nH="return 1.0 / (1.0 + exp(-1.0 * x));",rH=He({opSnippet:nH}),aH={kernelName:Fs,backendName:"webgl",kernelFunc:rH},sH=` +`,sj=Ge({opSnippet:aj}),ij={kernelName:Io,backendName:"webgl",kernelFunc:sj},oj="return 1.0 / (1.0 + exp(-1.0 * x));",lj=Ge({opSnippet:oj}),uj={kernelName:Ms,backendName:"webgl",kernelFunc:lj},cj=` if (isnan(x)) { return 0.0; } return sign(x); -`,iH=He({opSnippet:sH}),oH={kernelName:So,backendName:"webgl",kernelFunc:iH},lH=t_+` +`,hj=Ge({opSnippet:cj}),dj={kernelName:To,backendName:"webgl",kernelFunc:hj},pj=i_+` return sin(x); -`,uH=He({opSnippet:lH}),cH={kernelName:Rs,backendName:"webgl",kernelFunc:uH},hH=` +`,fj=Ge({opSnippet:pj}),mj={kernelName:Fs,backendName:"webgl",kernelFunc:fj},Aj=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,dH=He({opSnippet:hH}),pH={kernelName:No,backendName:"webgl",kernelFunc:dH},fH=` +`,yj=Ge({opSnippet:Aj}),gj={kernelName:So,backendName:"webgl",kernelFunc:yj},xj=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -3909,9 +3909,9 @@ return a / b;`,FU=` result = log(exp_x + 1.0); } return result; -`,mH=He({opSnippet:fH}),AH={kernelName:To,backendName:"webgl",kernelFunc:mH},yH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),A},gH={kernelName:ou,backendName:"webgl",kernelFunc:yH};function xH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new V_(u,l,a.shape.length,s.shape.length,c,[h,1],d),m=n.runWebGLProgram(p,[s,a,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var wH={kernelName:Rh,backendName:"webgl",kernelFunc:xH};function _H(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let m=Ku({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,m})}var bH={kernelName:Eo,backendName:"webgl",kernelFunc:_H},vH="return sqrt(x);",kH=He({opSnippet:vH}),IH={kernelName:Ms,backendName:"webgl",kernelFunc:kH},NH="return x * x;",SH=He({opSnippet:NH}),TH={kernelName:lu,backendName:"webgl",kernelFunc:SH},U_="return (a - b) * (a - b);",EH=Qt({opSnippet:U_,packedOpSnippet:U_}),CH={kernelName:Ds,backendName:"webgl",kernelFunc:EH};function RH({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=dr+` 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Nj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let m=Zu({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,m})}var Sj={kernelName:Co,backendName:"webgl",kernelFunc:Nj},Tj="return sqrt(x);",Ej=Ge({opSnippet:Tj}),Cj={kernelName:$s,backendName:"webgl",kernelFunc:Ej},Rj="return x * x;",Fj=Ge({opSnippet:Rj}),Mj={kernelName:uu,backendName:"webgl",kernelFunc:Fj},X_="return (a - b) * (a - b);",$j=Qt({opSnippet:X_,packedOpSnippet:X_}),Oj={kernelName:zs,backendName:"webgl",kernelFunc:$j};function Dj({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=fr+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new Ca(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var FH={kernelName:ga,backendName:"webgl",kernelFunc:RH},MH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=it(n.length),s=it(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new Ra(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var zj={kernelName:xa,backendName:"webgl",kernelFunc:Dj},Pj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=it(n.length),s=it(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${a} begin = ${a}(${e}); ${a} strides = ${a}(${t}); @@ -3919,15 +3919,15 @@ return a / b;`,FU=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return ab(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,_,M,b,w,x,N,T,E,L),D}var KG={kernelName:as,backendName:"wasm",setupFunc:qG,kernelFunc:XG},ZG=!1,YG=un(Zi,ZG,"bool"),JG=wn(is);function Cm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),pr({inputs:{x:a},backend:r,attrs:{shape:o}})}var QG={kernelName:Yi,backendName:"wasm",kernelFunc:Cm};function eq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var tq={kernelName:Ql,backendName:"wasm",kernelFunc:eq},sb;function nq(e){sb=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function rq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return sb(s,o,l,u,c,i),a}var aq={kernelName:Qi,backendName:"wasm",kernelFunc:rq,setupFunc:nq},sq=wn(os),iq=!1,oq=un(ls,iq),ib;function lq(e){ib=e.wasm.cwrap(us,null,["number","number","number","number","number","number","number"])}function uq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return ib(c,h,d,p,m,a,A),f}var cq={kernelName:us,backendName:"wasm",setupFunc:lq,kernelFunc:uq},ob;function hq(e){ob=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=Yu[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return ob(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,j,_,A,ie,m||0,te),ae}var pq={kernelName:Bs,backendName:"wasm",setupFunc:hq,kernelFunc:dq},lb;function fq(e){lb=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=Yu[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return lb(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,j,_,A,ie,m||0,te),ae}var Aq={kernelName:Vs,backendName:"wasm",setupFunc:fq,kernelFunc:mq},ub;function yq(e){ub=e.wasm.cwrap(to,null,["number","number","number","number","number","number","array","number"])}function gq(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=X1.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return ub(d,Mn[r.dtype],p,i,h,o,m,f),u}var xq={kernelName:to,backendName:"wasm",setupFunc:yq,kernelFunc:gq},cb;function wq(e){cb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function _q(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=pr({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=pr({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],m=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let f=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(m.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return cb(A,Mn[a.dtype],_,f,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var bq={kernelName:eo,backendName:"wasm",setupFunc:wq,kernelFunc:_q},vq=!1,kq=un(no,vq,"bool"),Iq=!1,Nq=un(cs,Iq,"bool"),hb;function Sq(e){hb=e.wasm.cwrap(ds,null,["number","number","number"])}function Tq(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;hb(a,n,i)}return s}var Eq={kernelName:ds,backendName:"wasm",setupFunc:Sq,kernelFunc:Tq},Cq=!1,Rq=un(io,Cq,"bool"),Fq=!1,Mq=un(oo,Fq,"bool"),$q=wn(ps),Oq=!1,Dq=un(uo,Oq,"bool"),db;function zq(e){db=e.wasm.cwrap(fs,null,["number, number, number"])}function Pq(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=_l(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",c,p);let[m,f]=C.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;db(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var Lq={kernelName:fs,backendName:"wasm",setupFunc:zq,kernelFunc:Pq},Wq=!1,Bq=un(ms,Wq),pb;function Vq(e){pb=e.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Uq(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=C.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,_=c.strideHeight,b=c.strideWidth,w=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(c.outShape,"float32"),T=r.dataIdMap.get(N.dataId).id;return pb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,m,f,A,y,g,_,b,w,x,T),N}var jq={kernelName:As,backendName:"wasm",setupFunc:Vq,kernelFunc:Uq},fb;function Hq(e){fb=e.wasm.cwrap(ys,null,["number, number, number"])}function Gq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=_l(i,a,t),m=h;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==o&&(u=c,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,A]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(A),g=u;u.dtype!=="float32"&&(g=tp({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let _=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(_.dataId).id;fb(l,y,b)}if(p&&t.disposeData(c.dataId),s){let b=C.expandShapeToKeepDim(_.shape,d);_.shape=b}return u.dtype!=="float32"&&t.disposeData(g.dataId),_}var qq={kernelName:ys,backendName:"wasm",setupFunc:Hq,kernelFunc:Gq},mb;function Xq(e){mb=e.wasm.cwrap(gs,null,["number, number, number"])}function Kq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=_l(i,a,t);if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",h,m);let[f,A]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;mb(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=C.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var Zq={kernelName:gs,backendName:"wasm",setupFunc:Xq,kernelFunc:Kq},Yq=!1,Jq=un(xs,Yq),Qq=!0,eX=un(ws,Qq),tX=wn(ho);function Rm(e,t){let n=new 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return ub(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,_,M,b,w,x,N,T,E,L),D}var eq={kernelName:ss,backendName:"wasm",setupFunc:JG,kernelFunc:QG},tq=!1,nq=un(Yi,tq,"bool"),rq=wn(os);function Rm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mr({inputs:{x:a},backend:r,attrs:{shape:o}})}var aq={kernelName:Ji,backendName:"wasm",kernelFunc:Rm};function sq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var iq={kernelName:eu,backendName:"wasm",kernelFunc:sq},cb;function oq(e){cb=e.wasm.cwrap(eo,null,["number","number","number","number","number","number"])}function lq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return cb(s,o,l,u,c,i),a}var uq={kernelName:eo,backendName:"wasm",kernelFunc:lq,setupFunc:oq},cq=wn(ls),hq=!1,dq=un(us,hq),hb;function pq(e){hb=e.wasm.cwrap(cs,null,["number","number","number","number","number","number","number"])}function fq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return hb(c,h,d,p,m,a,A),f}var mq={kernelName:cs,backendName:"wasm",setupFunc:pq,kernelFunc:fq},db;function Aq(e){db=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=Ju[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return db(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,H,_,A,ie,m||0,te),ae}var gq={kernelName:Vs,backendName:"wasm",setupFunc:Aq,kernelFunc:yq},pb;function xq(e){pb=e.wasm.cwrap(Us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=Ju[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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b=C.expandShapeToKeepDim(_.shape,d);_.shape=b}return u.dtype!=="float32"&&t.disposeData(g.dataId),_}var Jq={kernelName:gs,backendName:"wasm",setupFunc:Zq,kernelFunc:Yq},wb;function Qq(e){wb=e.wasm.cwrap(xs,null,["number, number, number"])}function eX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=bl(i,a,t);if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",h,m);let[f,A]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;wb(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=C.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var tX={kernelName:xs,backendName:"wasm",setupFunc:Qq,kernelFunc:eX},nX=!1,rX=un(ws,nX),aX=!0,sX=un(_s,aX),iX=wn(po);function Fm(e,t){let n=new 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new mr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new mr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new mr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Sl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=gr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Sl.className="Sequential";re.registerClass(Sl);function Kee(e){return new na(e)}function Zee(e){return new Sl(e)}function Yee(e,t){return t==null&&(t={}),qee(e,t)}function p3(e){return b3(e)}function Jee(e,t){sr.registerCallbackConstructor(e,t)}var $n=class extends re.Serializable{getConfig(){return{}}},Z3=class extends $n{apply(e,t=1){return CJ(e,t)}};Z3.className="elu";re.registerClass(Z3);var Y3=class extends $n{apply(e){return ud(e)}};Y3.className="selu";re.registerClass(Y3);var J3=class extends $n{apply(e){return Cr(e)}};J3.className="relu";re.registerClass(J3);var Q3=class extends $n{apply(e){return B(()=>tl(6,Cr(e)))}};Q3.className="relu6";re.registerClass(Q3);var e7=class extends $n{apply(e){return e}};e7.className="linear";re.registerClass(e7);var t7=class extends $n{apply(e){return In(e)}};t7.className="sigmoid";re.registerClass(t7);var n7=class extends $n{apply(e){return FJ(e)}};n7.className="hardSigmoid";re.registerClass(n7);var r7=class extends $n{apply(e){return Qo(e)}};r7.className="softplus";re.registerClass(r7);var a7=class extends $n{apply(e){return RJ(e)}};a7.className="softsign";re.registerClass(a7);var s7=class extends $n{apply(e){return Xo(e)}};s7.className="tanh";re.registerClass(s7);var xA=class extends $n{apply(e,t=-1){return Ou(e,t)}};xA.className="softmax";re.registerClass(xA);var i7=class extends $n{apply(e,t=-1){return nd(e,t)}};i7.className="logSoftmax";re.registerClass(i7);var o7=class extends $n{apply(e,t=1){return B(()=>In(e.mul(t)).mul(e))}};o7.className="swish";re.registerClass(o7);function Da(e){return e.getClassName()}function wA(e,t={}){return tc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function za(e){if(e==null){let t={};return t.className="linear",t.config={},wA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},wA(t)}else return e instanceof $n?e:wA(e)}function _A(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var l7=class extends re.Serializable{},pc=class extends l7{constructor(e){super();_A(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=Nt([1]);return this.hasL1&&(t=se(t,Ne(P(this.l1,Mt(e))))),this.hasL2&&(t=se(t,Ne(P(this.l2,ic(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};pc.className="L1L2";re.registerClass(pc);function Qee(e){return _A(e),new pc({l1:e!=null?e.l1:null,l2:0})}function ete(e){return _A(e),new pc({l2:e!=null?e.l2:null,l1:0})}var u7={l1l2:"L1L2"};function lt(e){return Om(e)}function c7(e,t={}){return tc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in u7?u7[e]:e,config:{}};return c7(t)}else return e instanceof l7?e:c7(e)}var bA=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Cr(e);return this.maxValue!=null&&(n=An(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};bA.className="ReLU";re.registerClass(bA);var vA=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Tu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};vA.className="LeakyReLU";re.registerClass(vA);var kA=class extends je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=zt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ot(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(kt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function h7(e,t){return B(()=>(kt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function tte(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=fr()),kt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Xh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Dr(o,n)),o})}function d7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=fr()),kt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=TA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ta.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function nte(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=fr()),kt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=h7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=yf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Dr(o,n)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var EA=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",EA.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Tl(t.kernelSize,e,"kernelSize"),this.strides=Tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,kt(this.dataFormat),this.activation=za(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=zt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=Tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Da(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fc=class extends EA{constructor(e,t){super(e,t);this.kernel=null,fc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=zt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return B(()=>{e=De(e);let n,r=this.bias==null?null:this.bias.read(),a=Yb(this.activation.getClassName());if(a!=null&&this.rank===2)n=d7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=tte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=d7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ot(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},mc=class extends fc{constructor(e){super(2,e);mc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};mc.className="Conv2D";re.registerClass(mc);var Tp=class extends fc{constructor(e){super(3,e);Tp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Tp.className="Conv3D";re.registerClass(Tp);var CA=class extends mc{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ot(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=De(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Sp(o,h,u,this.padding),m=Sp(l,d,c,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let A=Kh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=tt(A,[0,3,1,2])),this.bias!=null&&(A=Dr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Sp(t[r],o,s,this.padding),t[a]=Sp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};CA.className="Conv2DTranspose";re.registerClass(CA);var p7=class extends fc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=zt(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=zt(t.pointwiseConstraint)}build(e){if(e=ot(e),e.length{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=Of(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseConstraint),e.pointwiseConstraint=Dt(this.pointwiseConstraint),e}};p7.className="SeparableConv";var RA=class extends p7{constructor(e){super(2,e)}};RA.className="SeparableConv2D";re.registerClass(RA);var Ep=class extends fc{constructor(e){super(1,e);Ep.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zm(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Ep.className="Conv1D";re.registerClass(Ep);var FA=class extends je{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=sp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=sp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};FA.className="Cropping2D";re.registerClass(FA);var MA=class extends je{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};MA.className="UpSampling2D";re.registerClass(MA);function rte(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=fr()),kt(a);let i=TA(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Zo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var $A=class extends EA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=zt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=ot(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=De(e);let n=rte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=xr(t,this.kernelSize[0],this.padding,this.strides[0]),s=xr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseRegularizer),e}};$A.className="DepthwiseConv2D";re.registerClass($A);function f7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function m7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ar(2,l));if(t=tt(t,u),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Nn(a,-1)),a=tt(a,u)),r&&(t=En(t,0),a!=null&&(a=En(a,0)));let c=[],h,d=n,p=t.shape[0],m=tr(t),f;a!=null&&(f=tr(a));for(let y=0;ye(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=f[y],x=Tn(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),T=d.map((E,M)=>_[1][M].mul(w).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Cn(c,1)),[h,A,d]})}var zr=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new jt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ar(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){rA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new jt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let r=0;rWt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=f7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new jt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof yr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=m7((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=Nt(e.shape);return t=Ne(t,[1,2]),t=sc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Um(t,[1,n]):t):this.cell.stateSize>1?[Um(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=gr(r,n);return new e(Object.assign(t,{cell:a}))}};zr.className="RNN";re.registerClass(zr);var lc=class extends je{},Rp=class extends lc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0Tn(e),rate:this.dropout,training:r})),0Tn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Or(P(e,s),this.kernel.read()):a=Or(e,this.kernel.read()),this.bias!=null&&(a=Dr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Da(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Rp.className="SimpleRNNCell";re.registerClass(Rp);var OA=class extends zr{constructor(e){e.cell=new Rp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};OA.className="SimpleRNN";re.registerClass(OA);var Fp=class extends lc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0Tn(e),rate:this.dropout,training:n,count:3})),0Tn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};DA.className="GRU";re.registerClass(DA);var Ac=class extends lc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=vl([1,$a([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,$a([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ot(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends ar{apply(i,o){let l=a.apply([s]),u=new op().apply([s]),c=a.apply([s*2]);return o3(o3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Tn(e),rate:this.dropout,training:n,count:4})),0Tn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zA.className="LSTM";re.registerClass(zA);var Cp=class extends lc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{pi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(gr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return aA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;su3(t(),n),i=()=>oc(s,t,r);return!a||a<=1?Wt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Wt(o.clone()))}var ate=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Nt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(a)):this.states_=[Nt(a)];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(a)):this.states_[0]=Nt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let s=0;sWt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=xr(l,r[0],a,s[0],i[0]),h=xr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};A7.className="ConvRNN2D";var Mp=class extends Ac{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Tl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ut(o,"kernelSize")),this.strides=Tl(r||1,2,"strides"),this.strides.forEach(o=>Ut(o,"strides")),this.padding=a||"valid",Gn(this.padding),this.dataFormat=s||"channelsLast",kt(this.dataFormat),this.dilationRate=Tl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ut(o,"dilationRate"))}build(e){var t;e=ot(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ar{apply(c,h){let d=l.apply([u]),p=Er([u]),m=l.apply([u*2]);return Hm([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0Tn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Tn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Zt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,W,U]=Zt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,D),f=this.recurrentConv(f,L),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(c,f)),G=se(P(X,s),P(j,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ate(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Xr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Dr(a,n,this.dataFormat):a}recurrentConv(e,t){return Xr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Mp.className="ConvLSTM2DCell";re.registerClass(Mp);var PA=class extends A7{constructor(e){let t=new Mp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};PA.className="ConvLSTM2D";re.registerClass(PA);var $p=class extends je{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=De(e);if(0u3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$p.className="Dropout";re.registerClass($p);var LA=class extends $p{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};LA.className="SpatialDropout1D";re.registerClass(LA);var WA=class extends je{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=za(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=zt(e.kernelConstraint),this.biasConstraint=zt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ot(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ot(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e),r=Yb(this.activation.getClassName()),a;return r!=null?a=Or(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Or(n,this.kernel.read()),this.bias!=null&&(a=Dr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Da(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Dense";re.registerClass(WA);var BA=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ot(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Fa(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=$a(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=Ar(1,l).concat([0]);n.push(tt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=tt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(Ar(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Nn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Me("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>yc(a,e[s].shape.length)):r=[yc(this.axes,t.shape.length),yc(this.axes,n.shape.length)],this.normalize&&(t=xp(t,r[0]),n=xp(n,r[1])),ste(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[yc(this.axes,e.length),yc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ey.className="Dot";re.registerClass(ey);var ty=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e);return oc(()=>ip(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};ty.className="GaussianNoise";re.registerClass(ty);var ny=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e);return this.rate>0&&this.rate<1?oc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(ip(n.shape,1,r))},()=>n,t.training||!1):n})}};ny.className="GaussianDropout";re.registerClass(ny);var ry=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||De(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return oc(()=>{let r=De(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Na(nl(n),this.rate);o=ac(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>De(e),t.training||!1)}return e})}};ry.className="AlphaDropout";re.registerClass(ry);function gc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=l5(e,t,n,r,a,s);else if(e.rank===3)i=u5(e,t,n,r,a,s);else if(e.rank===4)i=c5(e,t,n,r,a,s);else throw new Me(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function ite(e,t,n,r,a=.001){return B(()=>{let s=ad(e,r),i=s.mean,o=s.variance;return[gc(e,i,o,n,t,a),i,o]})}function ote(e,t,n,r,a=.001){return B(()=>{let s=ad(e,r),i=s.mean,o=s.variance,l=[];for(let p of Ar(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[gc(e,u,c,d,h,a),i,o]})}function lte(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),Ar(0,e.rank-1))?ite(e,t,n,r,a):ote(e,t,n,r,a)}var ay=class extends je{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=zt(e.betaConstraint),this.gammaConstraint=zt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=ot(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new jt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,r=De(e),a=r.shape,s=a.length,i=Ar(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,Ar(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return gc(r,A,y,g,_,this.epsilon)}else return gc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,m]=lte(r,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{B(()=>{let _=1-g,b=A.read(),w=b.sub(y).mul(_);A.write(b.sub(w))})};return(()=>{f(this.movingMean,p,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Dt(this.betaConstraint),gammaConstraint:Dt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ay.className="BatchNormalization";re.registerClass(ay);var sy=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ot(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: 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integers.");if(n==null&&(n=fr()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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s==="max"?i=Cu(e,t,n,o):i=vu(e,t,n,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function y7(e,t,n,r,a,s){return B(()=>{kt(a),t3(s),Gn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=fr()),s==null&&(s="max"),e=h7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Tf(e,t,n,o):i=ff(e,t,n,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var g7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Gn(this.padding),this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){e=ot(e);let t=xr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=sc(De(e),2);let n=this.poolingFunction(De(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Sa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},oy=class extends g7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"max")}};oy.className="MaxPooling1D";re.registerClass(oy);var ly=class extends g7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"avg")}};ly.className="AveragePooling1D";re.registerClass(ly);var x7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),Gn(this.padding),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=xr(t,this.poolSize[0],this.padding,this.strides[0]),n=xr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},uy=class extends x7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"max")}};uy.className="MaxPooling2D";re.registerClass(uy);var cy=class extends x7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),Op(e,t,n,r,a,"avg")}};cy.className="AveragePooling2D";re.registerClass(cy);var w7=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),Gn(this.padding),this.inputSpec=[new jt({ndim:5})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=xr(t,this.poolSize[0],this.padding,this.strides[0]),n=xr(n,this.poolSize[1],this.padding,this.strides[1]),r=xr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},hy=class extends w7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),y7(e,t,n,r,a,"max")}};hy.className="MaxPooling3D";re.registerClass(hy);var dy=class extends w7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Gn(r),y7(e,t,n,r,a,"avg")}};dy.className="AveragePooling3D";re.registerClass(dy);var _7=class extends je{constructor(e){super(e);this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},py=class extends _7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return wt(n,1)})}};py.className="GlobalAveragePooling1D";re.registerClass(py);var fy=class extends _7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return Un(n,1)})}};fy.className="GlobalMaxPooling1D";re.registerClass(fy);var b7=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},my=class extends b7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?wt(n,[1,2]):wt(n,[2,3])})}};my.className="GlobalAveragePooling2D";re.registerClass(my);var Ay=class extends b7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?Un(n,[1,2]):Un(n,[2,3])})}};Ay.className="GlobalMaxPooling2D";re.registerClass(Ay);var v7=class extends je{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=gr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=gr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?hte:e.mergeMode,cte(this.mergeMode),e.weights)throw new Me("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Tl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=wr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Tl.className="Sequential";re.registerClass(Tl);function ete(e){return new ra(e)}function tte(e){return new Tl(e)}function nte(e,t){return t==null&&(t={}),Jee(e,t)}function g3(e){return S3(e)}function rte(e,t){or.registerCallbackConstructor(e,t)}var $n=class extends re.Serializable{getConfig(){return{}}},t7=class extends $n{apply(e,t=1){return OJ(e,t)}};t7.className="elu";re.registerClass(t7);var n7=class extends $n{apply(e){return cd(e)}};n7.className="selu";re.registerClass(n7);var r7=class extends $n{apply(e){return Fr(e)}};r7.className="relu";re.registerClass(r7);var a7=class extends $n{apply(e){return B(()=>nl(6,Fr(e)))}};a7.className="relu6";re.registerClass(a7);var s7=class extends $n{apply(e){return e}};s7.className="linear";re.registerClass(s7);var i7=class extends $n{apply(e){return In(e)}};i7.className="sigmoid";re.registerClass(i7);var o7=class extends $n{apply(e){return zJ(e)}};o7.className="hardSigmoid";re.registerClass(o7);var l7=class extends $n{apply(e){return el(e)}};l7.className="softplus";re.registerClass(l7);var u7=class extends $n{apply(e){return DJ(e)}};u7.className="softsign";re.registerClass(u7);var c7=class extends $n{apply(e){return Ko(e)}};c7.className="tanh";re.registerClass(c7);var wA=class extends $n{apply(e,t=-1){return Du(e,t)}};wA.className="softmax";re.registerClass(wA);var h7=class extends $n{apply(e,t=-1){return rd(e,t)}};h7.className="logSoftmax";re.registerClass(h7);var d7=class extends $n{apply(e,t=1){return B(()=>In(e.mul(t)).mul(e))}};d7.className="swish";re.registerClass(d7);function za(e){return e.getClassName()}function _A(e,t={}){return nc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Pa(e){if(e==null){let t={};return t.className="linear",t.config={},_A(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},_A(t)}else return e instanceof $n?e:_A(e)}function bA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var p7=class extends re.Serializable{},fc=class extends p7{constructor(e){super();bA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=St([1]);return this.hasL1&&(t=se(t,Ne(P(this.l1,Mt(e))))),this.hasL2&&(t=se(t,Ne(P(this.l2,oc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};fc.className="L1L2";re.registerClass(fc);function ate(e){return bA(e),new fc({l1:e!=null?e.l1:null,l2:0})}function ste(e){return bA(e),new fc({l2:e!=null?e.l2:null,l1:0})}var f7={l1l2:"L1L2"};function lt(e){return Dm(e)}function m7(e,t={}){return nc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in f7?f7[e]:e,config:{}};return m7(t)}else return e instanceof p7?e:m7(e)}var vA=class extends He{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Fr(e);return this.maxValue!=null&&(n=An(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};vA.className="ReLU";re.registerClass(vA);var kA=class extends He{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Eu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};kA.className="LeakyReLU";re.registerClass(kA);var IA=class extends He{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=zt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ot(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(It(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function A7(e,t){return B(()=>(It(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function ite(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=Ar()),It(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Kh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Pr(o,n)),o})}function y7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=Ar()),It(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=EA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ea.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function ote(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=Ar()),It(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=A7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=gf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Pr(o,n)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var CA=class extends He{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",CA.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=El(t.kernelSize,e,"kernelSize"),this.strides=El(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,It(this.dataFormat),this.activation=Pa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=zt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=El(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Dr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Pm(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:za(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},mc=class extends CA{constructor(e,t){super(e,t);this.kernel=null,mc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=zt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. 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Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=De(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Tp(o,h,u,this.padding),m=Tp(l,d,c,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let A=Zh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=tt(A,[0,3,1,2])),this.bias!=null&&(A=Pr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Tp(t[r],o,s,this.padding),t[a]=Tp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv2DTranspose";re.registerClass(RA);var g7=class extends mc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=zt(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=zt(t.pointwiseConstraint)}build(e){if(e=ot(e),e.length{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=Df(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseConstraint),e.pointwiseConstraint=Dt(this.pointwiseConstraint),e}};g7.className="SeparableConv";var FA=class extends g7{constructor(e){super(2,e)}};FA.className="SeparableConv2D";re.registerClass(FA);var Cp=class extends mc{constructor(e){super(1,e);Cp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Pm(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Cp.className="Conv1D";re.registerClass(Cp);var MA=class extends He{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};MA.className="Cropping2D";re.registerClass(MA);var $A=class extends He{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,TJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$A.className="UpSampling2D";re.registerClass($A);function lte(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=Ar()),It(a);let i=EA(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Yo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var OA=class extends CA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=zt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=ot(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=De(e);let n=lte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=_r(t,this.kernelSize[0],this.padding,this.strides[0]),s=_r(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseRegularizer),e}};OA.className="DepthwiseConv2D";re.registerClass(OA);function x7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function w7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(gr(2,l));if(t=tt(t,u),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Nn(a,-1)),a=tt(a,u)),r&&(t=En(t,0),a!=null&&(a=En(a,0)));let c=[],h,d=n,p=t.shape[0],m=rr(t),f;a!=null&&(f=rr(a));for(let y=0;ye(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=f[y],x=Tn(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),T=d.map((E,M)=>_[1][M].mul(w).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Cn(c,1)),[h,A,d]})}var Lr=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Rp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ht({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){aA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ht({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ta("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>St([n,r])):this.states_=[St([n,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>St([n,r])):this.states_[0]=St([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let r=0;rWt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=x7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ht({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof xr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=w7((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=St(e.shape);return t=Ne(t,[1,2]),t=ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Hm(t,[1,n]):t):this.cell.stateSize>1?[Hm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Lr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=wr(r,n);return new e(Object.assign(t,{cell:a}))}};Lr.className="RNN";re.registerClass(Lr);var uc=class extends He{},Fp=class extends uc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0Tn(e),rate:this.dropout,training:r})),0Tn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=zr(P(e,s),this.kernel.read()):a=zr(e,this.kernel.read()),this.bias!=null&&(a=Pr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,zr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Fp.className="SimpleRNNCell";re.registerClass(Fp);var DA=class extends Lr{constructor(e){e.cell=new Fp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};DA.className="SimpleRNN";re.registerClass(DA);var Mp=class extends uc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0Tn(e),rate:this.dropout,training:n,count:3})),0Tn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zA.className="GRU";re.registerClass(zA);var yc=class extends uc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=kl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ot(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends ir{apply(i,o){let l=a.apply([s]),u=new lp().apply([s]),c=a.apply([s*2]);return d3(d3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Tn(e),rate:this.dropout,training:n,count:4})),0Tn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};PA.className="LSTM";re.registerClass(PA);var Rp=class extends uc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{fi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(wr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return sA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sf3(t(),n),i=()=>lc(s,t,r);return!a||a<=1?Wt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Wt(o.clone()))}var ute=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=St(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ta("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>St(a)):this.states_=[St(a)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>St(a)):this.states_[0]=St(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). 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Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ir{apply(c,h){let d=l.apply([u]),p=Rr([u]),m=l.apply([u*2]);return Gm([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0Tn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Tn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Zt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,W,U]=Zt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,D),f=this.recurrentConv(f,L),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(c,f)),G=se(P(X,s),P(H,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ute(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Kr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Pr(a,n,this.dataFormat):a}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};$p.className="ConvLSTM2DCell";re.registerClass($p);var LA=class extends _7{constructor(e){let t=new $p(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};LA.className="ConvLSTM2D";re.registerClass(LA);var Op=class extends He{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=De(e);if(0f3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Op.className="Dropout";re.registerClass(Op);var WA=class extends Op{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};WA.className="SpatialDropout1D";re.registerClass(WA);var BA=class extends He{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=Pa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=zt(e.kernelConstraint),this.biasConstraint=zt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ot(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ot(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=De(e),r=n3(this.activation.getClassName()),a;return r!=null?a=zr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=zr(n,this.kernel.read()),this.bias!=null&&(a=Pr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};BA.className="Dense";re.registerClass(BA);var VA=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ot(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Ma(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Oa(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=gr(1,l).concat([0]);n.push(tt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=tt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(gr(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Nn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Me("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>gc(a,e[s].shape.length)):r=[gc(this.axes,t.shape.length),gc(this.axes,n.shape.length)],this.normalize&&(t=wp(t,r[0]),n=wp(n,r[1])),cte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[gc(this.axes,e.length),gc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ty.className="Dot";re.registerClass(ty);var ny=class extends 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He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=zt(e.betaConstraint),this.gammaConstraint=zt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=ot(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ht({ndim:e.length,axes:{[t]:n}})];let 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t=_r(t,this.poolSize[0],this.padding,this.strides[0]),n=_r(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},cy=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),Dp(e,t,n,r,a,"max")}};cy.className="MaxPooling2D";re.registerClass(cy);var hy=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),Dp(e,t,n,r,a,"avg")}};hy.className="AveragePooling2D";re.registerClass(hy);var I7=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),qn(this.padding),this.inputSpec=[new Ht({ndim:5})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=_r(t,this.poolSize[0],this.padding,this.strides[0]),n=_r(n,this.poolSize[1],this.padding,this.strides[1]),r=_r(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},dy=class extends I7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),b7(e,t,n,r,a,"max")}};dy.className="MaxPooling3D";re.registerClass(dy);var py=class extends I7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return It(a),qn(r),b7(e,t,n,r,a,"avg")}};py.className="AveragePooling3D";re.registerClass(py);var N7=class extends He{constructor(e){super(e);this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},fy=class extends N7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return wt(n,1)})}};fy.className="GlobalAveragePooling1D";re.registerClass(fy);var my=class extends N7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=De(e);return Hn(n,1)})}};my.className="GlobalMaxPooling1D";re.registerClass(my);var S7=class extends He{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,It(this.dataFormat),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ay=class extends S7{call(e,t){return B(()=>{let n=De(e);return this.dataFormat==="channelsLast"?wt(n,[1,2]):wt(n,[2,3])})}};Ay.className="GlobalAveragePooling2D";re.registerClass(Ay);var yy=class extends S7{call(e,t){return B(()=>{let n=De(e);return 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r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[nt(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Zs(r,fe(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[Zs(s,fe(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Sa(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Sa(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:H(l,s)});return[Cn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return tr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Ia(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Zt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[D5(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[z5(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[Uf(r,s,a,s.dtype===i.dtype?i:fe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_ne=(e,t,n)=>{switch(e.op){case"FFT":return[Du(k("x",e,t,n))];case"IFFT":return[rl(k("x",e,t,n))];case"RFFT":return[zu(k("x",e,t,n))];case"IRFFT":return[fd(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bne=(e,t,n)=>{switch(e.op){case"Cast":return[fe(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[Nn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Sa(k("x",e,t,n),r)]}case"Reshape":return[H(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[Ef(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Kr(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[Ru(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[ku(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[gf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Iu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function tv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>ene(s,i,o));case"basic_math":return B(()=>tne(s,i,o));case"control":return one(s,i,o);case"convolution":return B(()=>lne(s,i,o));case"creation":return B(()=>une(s,i,o));case"dynamic":return cne(s,i,o);case"evaluation":return B(()=>hne(s,i,o));case"image":return B(()=>mne(s,i,o));case"graph":return B(()=>dne(s,i,o));case"logical":return B(()=>Ane(s,i,o));case"matrices":return B(()=>yne(s,i,o));case"normalization":return B(()=>gne(s,i,o));case"reduction":return B(()=>xne(s,i,o));case"slice_join":return B(()=>wne(s,i,o));case"spectral":return B(()=>_ne(s,i,o));case"transformation":return B(()=>bne(s,i,o));case"hash_table":return fne(s,i,o,r);case"custom":let l=R7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Qte(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var nv=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function av(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((rv(d)||vne(d)||kne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ine(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Nne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Sne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Tne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function rv(e){return Nne.indexOf(e.op)>=0}function vne(e){return Sne.indexOf(e.op)>=0}function kne(e){return Tne.indexOf(e.op)>=0}var My=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new My(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=av(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Ine(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new nv(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=On(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;mvn(m,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=$te(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new nv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>vn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=av(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=On(g),w=[];w[b]=e[g],p[_]=w});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!rv(g)&&!vn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=ra(c.node.name,n)),r[c.node.name]==null){let d=tv(c.node,r,n,this._resourceManager);h||([h]=ra(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ra(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ene=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Cne="?tfjs-format=file",Rne="model.json",sv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ene}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=mn.browserHTTPRequest(e,this.loadOptions);else{let t=mn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(mn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=mn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new My(Z7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Z7.Instance.transformGraph(e.modelInitializer);this.initializer=new My(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=mn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Xe)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Tt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Rne}${Cne}`);let n=new sv(e,t);return await n.load(),n}var Fne="3.2.0",iv={};Oe(iv,{CSVDataset:()=>lv,Dataset:()=>El,FileDataSource:()=>uv,TextLineDataset:()=>ov,URLDataSource:()=>cv,array:()=>Mne,csv:()=>One,func:()=>Dne,generator:()=>zne,microphone:()=>Lne,version_data:()=>Wne,webcam:()=>Pne,zip:()=>$ne});var Bne=Mi(J2()),Vne=Mi(J2());function Une(e,t){return Lp(e,t)}function Lp(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Cl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=Lp(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else 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Ht{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},tre=class extends Ht{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},rre=class extends Ht{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},are=class extends Ht{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Se(e.value)}}},sre=class extends Ht{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ur.getTensorsInContainer(e.value),n=this.transform(e.value),r=ur.getTensorsInContainer(n);for(let a of t)ur.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},ire=class extends Ht{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},yv=class extends Ht{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ur.getTensorsInContainer(e.value),n=await this.transform(e.value),r=ur.getTensorsInContainer(n);for(let a of t)ur.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Dy=class extends Ht{constructor(){super();this.outputQueue=new $y,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},ore=class extends Dy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ur.getTensorsInContainer(e.value),n=this.transform(e.value),r=ur.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)ur.isTensorInList(a,r)||a.dispose();return!0}},Av=class extends Ht{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},La;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(La||(La={}));var Jne=class extends Ht{constructor(e,t=La.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Ht?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await pv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case La.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new _v(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),cr(n,t)}},bv=class extends Ht{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Bt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=gn([s,a,o,i],[1,4])}else this.cropBox=gn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new bv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Uo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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kv{constructor(e,t){super();this.upstream=e,this.impl=new dre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dre=class extends Dy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},fre=class extends Ht{decodeUTF8(){return new pre(this)}},pre=class extends kv{constructor(e){super();this.upstream=e,this.impl=new mre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mre=class extends Dy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=y8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Iv=class extends fre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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vv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Nv(this.url)?new uv(this.url,this.fileOptions).iterator():yre(this.url,this.fileOptions)}};function One(e,t={}){return new lv(new cv(e),t)}function Dne(e){let t=Oy(e);return Dn(async()=>t)}function zne(e){return Dn(async()=>{let t=await e();return Oy(()=>t.next())})}async function Pne(e,t){return bv.create(e,t)}async function Lne(e){return _v.create(e)}var Wne="3.2.0",gre={tfjs:g8,"tfjs-core":x8,"tfjs-data":w8,"tfjs-layers":_8,"tfjs-converter":b8,"tfjs-backend-cpu":Sx,"tfjs-backend-webgl":Xw,"tfjs-backend-wasm":Pb};var zn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Sv(){if(!nf(zn.name)){Te("backend registration:",zn.name);try{zn.canvas=typeof OffscreenCanvas!="undefined"?new 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r=Ee(e,[0,1],[-1,2]),a=se(r,t),s=Ee(e,[0,3],[-1,2]),i=we(s,n),o=we(a,n),l=we(i,2),u=me(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return Ko([h,d],1)}var Ev=class{constructor(t,n){this.blazeFaceModel=t,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=xre(n.face.detector.inputSize),this.anchors=gn(this.anchorsData),this.inputSize=Bt([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let h=t.resizeBilinear([this.width,this.height]),d=me(h.div(127.5),1),p=this.blazeFaceModel.predict(d),m;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),_=nt([g[0],g[2]],2),b=nt([g[1],g[3]],2);m=nt([b,_],1).squeeze(0)}else m=p.squeeze();let f=_re(m,this.anchors,this.inputSize),A=Ee(m,[0,0],[-1,1]),y=In(A).squeeze();return[m,f,y]}),s=await Ke.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Ee(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;hthis.config.face.detector.minConfidence){let m=wre(l[h]),f=this.anchorsData[d],A=B(()=>Ee(n,[d,Tv-1],[1,-1]).squeeze().reshape([Tv,-1]));c.push({box:m,landmarks:A,anchor:f,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),n.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.width,t.shape[1]/this.height]}}};async function Cv(e){let t=await Tt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new Ev(t,e);return e.debug&&Te(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function Rv(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function bc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function vc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Py(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ke.cropAndResize(t,s,[0],n)}function Vp(e,t=1.6){let n=vc(e),r=bc(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function Up(e){let t=vc(e),n=bc(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}var jp=[[1,0,0],[0,1,0],[0,0,1]];function bre(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Fv(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return bre(n)}function Mv(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Wa(e,t){let n=0;for(let 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s&&(l=Ke.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=B(()=>this.storedBoxes.map((i,o)=>{let l,u=0,c;if(n.face.detector.rotation){let[w,x]=i.landmarks.length>=Sre?Ere:Fre;u=Fv(i.landmarks[w],i.landmarks[x]);let N=vc({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=Ke.rotateWithOffset(t,u,0,T);c=Ly(-u,N),l=Py({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshHeight,this.meshWidth]).div(255)}else{c=jp;let w=t.clone();l=Py({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,confidence:i.confidence,image:l};let[,h,d]=this.meshDetector.predict(l),p=h.dataSync()[0];if(pi!==null),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var z6=Zc(Lv());var Zy={};lr(Zy,{FaceBoxes:()=>Yy,load:()=>zre});var Ky={};function Wr(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Ky[e]=i,Te("Human profiler",e,i)}var Yy=class{constructor(t,n){this.enlarge=1.1,this.model=t,this.config=n}async estimateFaces(t,n){n&&(this.config=n);let r=[],a=Ke.resizeBilinear(t,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),s=a.toInt(),i,o;if(n.profile){let l=await 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t2={};lr(t2,{load:()=>s2,predict:()=>i2});var wi,n2={gender:""},Xp=Number.MAX_SAFE_INTEGER,r2=!1,a2=[.2989,.587,.114];async function s2(e){return wi||(wi=await Tt(e.face.gender.modelPath),r2=wi.inputs[0].shape[3]===1,e.debug&&Te(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),wi}async function i2(e,t){return wi?Xp{let r=Ke.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;r2?a=B(()=>{let[o,l,u]=Zt(r,3,3),c=P(o,a2[0]),h=P(l,a2[1]),d=P(u,a2[2]);return Go([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Se(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await wi.predict(a));else{let o=t.face.gender.enabled?await hr(()=>wi.predict(a)):{};s=o.result.clone(),o.result.dispose(),Wr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(r2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let 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Jv(t.heatmapScores,t.offsets,n)],l=k2(o,[a,s],[n.body.inputSize,n.body.inputSize]);r(l)})}var N2=class{constructor(t){this.baseModel=t}async estimatePoses(t,n){let r=n6(t,[n.body.inputSize,n.body.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await rae(t,a,n):await nae(t,a,n);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function S2(e){let t=await Tt(e.body.modelPath),n=new f2(t);return e.debug&&Te(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new N2(n)}var F2={};lr(F2,{HandPose:()=>$2,load:()=>O2});function Qp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function kc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function r6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ke.cropAndResize(t,s,[0],n)}function a6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function e0(e,t=1.5){let n=kc(e),r=Qp(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function t0(e){let t=kc(e),n=Qp(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var T2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=gn(this.anchors),this.inputSizeTensor=Bt([n,n]),this.doubleInputSizeTensor=Bt([n*2,n*2])}normalizeBoxes(t){return B(()=>{let n=Ee(t,[0,0],[-1,2]),r=Ee(t,[0,2],[-1,2]),a=se(we(n,this.inputSizeTensor),this.anchorsTensor),s=we(r,this.doubleInputSizeTensor),i=P(me(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return Ko([i,o],1)})}normalizeLandmarks(t,n){return B(()=>{let r=se(we(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=B(()=>In(Ee(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Ee(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let u=await Ke.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),c=u.arraySync();s.dispose(),u.dispose();let h=[];for(let d of c)if(i[d]>=n.hand.minConfidence){let p=Ee(l,[d,0],[1,-1]),m=Ee(a,[d,5],[1,14]),f=B(()=>this.normalizeLandmarks(m,d).reshape([-1,2]));m.dispose(),h.push({box:p,palmLandmarks:f,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=B(()=>t.resizeBilinear([n.hand.inputSize,n.hand.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),c=u.slice(0,2),h=u.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(a6({startPoint:c,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/n.hand.inputSize,r/n.hand.inputSize]))}return o}};function aae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function s6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return aae(n)}var i6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ba(e,t){let n=0;for(let r=0;rC2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return e0(t0(a),iae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=e0(t0(n),u6);r.palmLandmarks=[];for(let a=0;a[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=E2(r,[0,0]),u=o.map(p=>[...C2(p,l),p[2]]),c=l6(a),h=[...kc(n),1],d=[Ba(h,c[0]),Ba(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i=n.hand.minConfidence){let _=H(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let N={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=e0(t0(o),u6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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R2(r,n,e.hand.inputSize),s=new $2(a);return e.hand.enabled&&e.debug&&Te(`load model: ${e.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),e.hand.landmarks&&e.debug&&Te(`load model: ${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var d6=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},p6=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},f6=e=>{if(!e)return[];let t=[];for(let n=0;n{if(!e)return[];let t=[];for(let n=0;n0){let a=r.reduce((i,o)=>i.position[2]i.position[1](u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let 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r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wne=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[je.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[je.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=k("image",e,t,n),a=k("boxes",e,t,n),s=k("boxInd",e,t,n),i=k("cropSize",e,t,n),o=k("method",e,t,n),l=k("extrapolationValue",e,t,n);return[je.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_ne=(e,t,n)=>{switch(e.op){case"Equal":return[Ia(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[ei(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[tr(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[Sa(k("a",e,t,n),k("b",e,t,n))];case"Less":return[ed(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[Js(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[nr(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[Cu(k("a",e,t,n))];case"LogicalOr":return[ad(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[yn(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bne=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ue(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[tt(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=k("args",e,t,n);return[Ea.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vne=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Zs(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[Zs(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[If(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[Du(k("x",e,t,n))];case"LogSoftmax":return[rd(k("x",e,t,n))];case"SparseToDense":return[Hf(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kne=(e,t,n)=>{switch(e.op){case"Max":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Hn(k("x",e,t,n),i,o)]}case"Mean":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[wt(k("x",e,t,n),i,o)]}case"Min":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[tl(k("x",e,t,n),i,o)]}case"Sum":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Ne(k("x",e,t,n),i,o)]}case"All":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[qh(k("x",e,t,n),i,o)]}case"Any":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[_u(k("x",e,t,n),i,o)]}case"ArgMax":{let i=k("axis",e,t,n);return[bu(k("x",e,t,n),i)]}case"ArgMin":{let i=k("axis",e,t,n);return[of(k("x",e,t,n),i)]}case"Prod":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[id(k("x",e,t,n),i,o)]}case"Cumsum":{let i=k("axis",e,t,n),o=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[Jh(k("x",e,t,n),i,o,l)]}case"Bincount":let r=k("x",e,t,n),a=k("weights",e,t,n),s=k("size",e,t,n);return[A5(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),u=k("binaryOutput",e,t,n);return[_5(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ine=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[nt(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Ys(r,fe(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[Ys(s,fe(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Ta(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Ta(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:j(l,s)});return[Cn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return rr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Na(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Zt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[B5(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[V5(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[Hf(r,s,a,s.dtype===i.dtype?i:fe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nne=(e,t,n)=>{switch(e.op){case"FFT":return[zu(k("x",e,t,n))];case"IFFT":return[al(k("x",e,t,n))];case"RFFT":return[Pu(k("x",e,t,n))];case"IRFFT":return[md(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Sne=(e,t,n)=>{switch(e.op){case"Cast":return[fe(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[Nn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Ta(k("x",e,t,n),r)]}case"Reshape":return[j(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[Cf(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Zr(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[Fu(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[Iu(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[xf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Nu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function iv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>sne(s,i,o));case"basic_math":return B(()=>ine(s,i,o));case"control":return dne(s,i,o);case"convolution":return B(()=>pne(s,i,o));case"creation":return B(()=>fne(s,i,o));case"dynamic":return mne(s,i,o);case"evaluation":return B(()=>Ane(s,i,o));case"image":return B(()=>wne(s,i,o));case"graph":return B(()=>yne(s,i,o));case"logical":return B(()=>_ne(s,i,o));case"matrices":return B(()=>bne(s,i,o));case"normalization":return B(()=>vne(s,i,o));case"reduction":return B(()=>kne(s,i,o));case"slice_join":return B(()=>Ine(s,i,o));case"spectral":return B(()=>Nne(s,i,o));case"transformation":return B(()=>Sne(s,i,o));case"hash_table":return xne(s,i,o,r);case"custom":let l=D7(s.op);if(l&&l.customExecutor)return l.customExecutor(new ane(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function uv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>On(d)[0]),c=[];r!=null&&(c=r.map(d=>On(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((lv(d)||Tne(d)||Ene(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Cne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>On(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Rne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Fne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Mne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function lv(e){return Rne.indexOf(e.op)>=0}function Tne(e){return Fne.indexOf(e.op)>=0}function Ene(e){return Mne.indexOf(e.op)>=0}var $y=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new $y(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=uv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Cne(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[On(c)[0]]),a=t.map(c=>On(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new ov(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=On(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;mvn(m,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Lte(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new ov(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>vn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[On(g)[0]]),i=n.map(g=>On(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=uv(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=On(g),w=[];w[b]=e[g],p[_]=w});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!lv(g)&&!vn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=aa(c.node.name,n)),r[c.node.name]==null){let d=iv(c.node,r,n,this._resourceManager);h||([h]=aa(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=aa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=On(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=On(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},$ne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},One="?tfjs-format=file",Dne="model.json",cv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new $ne}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=mn.browserHTTPRequest(e,this.loadOptions);else{let t=mn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(mn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=mn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new $y(tv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=tv.Instance.transformGraph(e.modelInitializer);this.initializer=new $y(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=mn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ke)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function kt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Dne}${One}`);let n=new cv(e,t);return await n.load(),n}var zne="3.2.0",hv={};Oe(hv,{CSVDataset:()=>pv,Dataset:()=>Cl,FileDataSource:()=>fv,TextLineDataset:()=>dv,URLDataSource:()=>mv,array:()=>Pne,csv:()=>Wne,func:()=>Bne,generator:()=>Vne,microphone:()=>Hne,version_data:()=>jne,webcam:()=>Une,zip:()=>Lne});var Gne=$i(rg()),qne=$i(rg());function Xne(e,t){return Wp(e,t)}function Wp(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Rl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=Wp(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Kne(e,t=yv){return Av(e,t)}function Av(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Rl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(u=>u[i]),l=Av(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function yv(e){return e===null?null:Rl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function gv(e,t){let n=new Map;Wp(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return Wp(e,t,n)}function Rl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ke))}function Yne(e){return e==null||Zne(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ke||v.isTypedArray(e)}function Zne(e){return e===null||typeof e!="object"&&typeof e!="function"}function Qne(e){return Xne(e,Jne)}function Jne(e){return e instanceof Ke?{value:e.clone(),recurse:!1}:Rl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var xv=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},Oy=class extends xv{constructor(){super(Oy.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new lre(this,e,t)}columnMajorBatch(e,t=!0,n=yv){return this.rowMajorBatch(e,t).map(r=>Kne(r,n))}concatenate(e,t){return new _v(wv([this,e]),t)}take(e){return e<0||e==null?this:new ore(this,e)}skip(e){return e<0||e==null?this:new ire(this,e)}prefetch(e){return new vv(this,e)}shuffle(e,t){return new pre(this,e,t)}serial(){return new sre(this)}},ere=class extends jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Qne(e),done:!1}}},tre=class extends jt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},sre=class extends jt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},ire=class extends jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},lre=class extends jt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},ure=class extends jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},cre=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},hre=class extends jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},bv=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},zy=class extends jt{constructor(){super();this.outputQueue=new Oy,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},dre=class extends zy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return!0}},_v=class extends jt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Wa;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Wa||(Wa={}));var rre=class extends jt{constructor(e,t=Wa.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await gv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Wa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Wa.SHORTEST:return{value:null,done:!0};case Wa.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},vv=class extends jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new xv(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},pre=class extends vv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qne.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Cl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Dn(async()=>(await n.iterator()).columnMajorBatch(e,t,fre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Dn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Dn(async()=>(await t.iterator()).filter(r=>B(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Dn(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Dn(async()=>{let r=Dy(async()=>({value:await t.iterator(),done:!1}));return nre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Gne.alea(t||v.now().toString());return Dn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Dn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Cl.MAX_BUFFER_SIZE=1e4;function Dn(e,t=null){return new class extends Cl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Pne(e){return Dn(async()=>wv(e),e.length)}function Lne(e){if(!Rl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await gv(e,r=>{if(r instanceof Cl)return{value:r.iterator(),recurse:!1};if(Rl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return are(n,Wa.SHORTEST)},t)}function fre(e){if(e===null)return null;let t=e[0];return Yne(t)?{value:mre(e),recurse:!1}:{value:null,recurse:!0}}function mre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ke?Cn(e):pr(e)}var dv=class extends Cl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bp='"',bc=Symbol("out"),kv=Symbol("field"),Vp=Symbol("quote"),Py=Symbol("quoteafterquote"),Iv=Symbol("quoteinquote"),pv=class extends Cl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new dv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Nv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),pr(n,t)}},Sv=class extends jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Bt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=gn([s,a,o,i],[1,4])}else this.cropBox=gn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Sv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ho.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=Nn(fe(e,"float32"),0),n;n=je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Tv=class{},Ev=class extends jt{split(e){return new Are(this,e)}},Are=class extends Ev{constructor(e,t){super();this.upstream=e,this.impl=new yre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yre=class extends zy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},xre=class extends jt{decodeUTF8(){return new gre(this)}},gre=class extends Ev{constructor(e){super();this.upstream=e,this.impl=new wre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wre=class extends zy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=b8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Cv=class extends xre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function bre(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=_re(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new Cv(s,t)}else throw new Error(a.statusText)}var _re=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Rv(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var fv=class extends Tv{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Rv(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Cv(this.input,this.options)}},mv=class extends Tv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Rv(this.url)?new fv(this.url,this.fileOptions).iterator():bre(this.url,this.fileOptions)}};function Wne(e,t={}){return new pv(new mv(e),t)}function Bne(e){let t=Dy(e);return Dn(async()=>t)}function Vne(e){return Dn(async()=>{let t=await e();return Dy(()=>t.next())})}async function Une(e,t){return Sv.create(e,t)}async function Hne(e){return Nv.create(e)}var jne="3.2.0",vre={tfjs:v8,"tfjs-core":k8,"tfjs-data":I8,"tfjs-layers":N8,"tfjs-converter":S8,"tfjs-backend-cpu":Fx,"tfjs-backend-webgl":Qw,"tfjs-backend-wasm":Ub};var zn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Fv(){if(!rf(zn.name)){Se("backend registration:",zn.name);try{zn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(zn.width,zn.height):document.createElement("canvas")}catch(e){Se("error: cannot create canvas:",e);return}try{zn.gl=zn.canvas.getContext("webgl2",zn.webGLattr)}catch(e){Se("error: cannot get WebGL2 context:",e);return}try{Wd(2,zn.gl)}catch(e){Se("error: cannot set WebGL2 context:",e);return}try{let e=new Hd(zn.gl);Go(zn.name,()=>new gl(e),zn.priority)}catch(e){Se("error: cannot register WebGL backend:",e);return}try{Po("webgl").forEach(t=>{let n={...t,backendName:zn.name};Hs(n)})}catch(e){Se("error: cannot update WebGL backend registration:",e);return}try{Xa.set("WEBGL_VERSION",2)}catch(e){Se("error: cannot set WebGL backend flags:",e);return}Se("backend registered:",zn.name)}}var Mv=6;function kre(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r({startEndTensor:e,startPoint:Ee(e,[0,0],[-1,2]),endPoint:Ee(e,[0,2],[-1,2])});function Nre(e,t,n){let r=Ee(e,[0,1],[-1,2]),a=se(r,t),s=Ee(e,[0,3],[-1,2]),i=ye(s,n),o=ye(a,n),l=ye(i,2),u=me(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return Zo([h,d],1)}var $v=class{constructor(t,n){this.blazeFaceModel=t,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=kre(n.face.detector.inputSize),this.anchors=gn(this.anchorsData),this.inputSize=Bt([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let h=t.resizeBilinear([this.width,this.height]),d=me(h.div(127.5),1),p=this.blazeFaceModel.predict(d),m;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),_=nt([g[0],g[2]],2),b=nt([g[1],g[3]],2);m=nt([b,_],1).squeeze(0)}else m=p.squeeze();let f=Nre(m,this.anchors,this.inputSize),A=Ee(m,[0,0],[-1,1]),y=In(A).squeeze();return[m,f,y]}),s=await je.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Ee(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;hthis.config.face.detector.minConfidence){let m=Ire(l[h]),f=this.anchorsData[d],A=B(()=>Ee(n,[d,Mv-1],[1,-1]).squeeze().reshape([Mv,-1]));c.push({box:m,landmarks:A,anchor:f,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),n.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.width,t.shape[1]/this.height]}}};async function Ov(e){let t=await kt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new $v(t,e);return e.debug&&Se(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function Dv(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function 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Yy={};Zn(Yy,{FaceBoxes:()=>Jy,load:()=>Vre});var Zy={};function ur(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Zy[e]=i,Se("Human profiler",e,i)}var Jy=class{constructor(t,n){this.enlarge=1.1,this.model=t,this.config=n}async estimateFaces(t,n){n&&(this.config=n);let r=[],a=je.resizeBilinear(t,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),s=a.toInt(),i,o;if(n.profile){let l=await 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n2={};Zn(n2,{load:()=>i2,predict:()=>o2});var _i,r2={gender:""},Kp=Number.MAX_SAFE_INTEGER,a2=!1,s2=[.2989,.587,.114];async function i2(e){return _i||(_i=await kt(e.face.gender.modelPath),a2=_i.inputs[0].shape[3]===1,e.debug&&Se(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),_i}async function o2(e,t){return _i?Kp{let r=je.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;a2?a=B(()=>{let[o,l,u]=Zt(r,3,3),c=P(o,s2[0]),h=P(l,s2[1]),d=P(u,s2[2]);return qo([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Te(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await _i.predict(a));else{let o=t.face.gender.enabled?await Vn(()=>_i.predict(a)):{};s=o.result.clone(),o.result.dispose(),ur("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(a2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let 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a=0;a[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=C2(r,[0,0]),u=o.map(p=>[...R2(p,l),p[2]]),c=f6(a),h=[...Ic(n),1],d=[Va(h,c[0]),Va(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i=n.hand.minConfidence){let _=j(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let N={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else 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kt(e.body.modelPath),cr.width=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[2].size),cr.height=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Se(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),cr}async function L2(e,t){if(!cr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=je.resizeBilinear(e,[cr.width||t.body.inputSize,cr.height||t.body.inputSize],!1),a=ye(r,[255]);r.dispose();let s;if(t.profile){let o=await Vn(()=>cr.predict(a));s=o.result.find(l=>l.size===195).dataSync(),o.result.forEach(l=>l.dispose()),ur("blazepose",o)}else{let o=await cr.predict(a);s=o.find(l=>l.size===195||l.size===155).dataSync(),o.forEach(l=>l.dispose())}a.dispose();let i=[];for(let o=0;o{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},x6=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let 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e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Ic(s,i):n[a]=i}),n),{})}var P2=class{constructor(t={}){this.tf=Yc,this.package=D2,this.version=z2,this.config=Ic(g6,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=z6,this.age=Jy,this.gender=t2,this.emotion=o2,this.body=I2,this.hand=F2}profile(){return this.config.profile?Ky:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Te(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?Bv(t,n):0}async load(t=null){this.state="load";let n=ut();t&&(this.config=Ic(this.config,t)),this.firstRun&&(this.config.debug&&Te(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Te("configuration:",this.config),this.config.debug&&Te("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Zy:z6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Qy(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?s2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?c2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?d2(this.config):null),this.models.posenet||(this.config.body.enabled?S2(this.config):null),this.models.handpose||(this.config.hand.enabled?O2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Qy(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await s2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await c2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await d2(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await S2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await O2(this.config))),this.firstRun&&(this.config.debug&&Te("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(ut()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ut();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Te("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Te("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Te("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Sv();try{await this.tf.setBackend(this.config.backend)}catch(r){Te("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Te("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Te(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ut()-n)}}async detectFace(t){var u,c,h,d,p,m;let n,r,a,s,i,o=[];this.state="run:face",n=ut();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));this.perf.face=Math.trunc(ut()-n);for(let f of l){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Te("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?e2(f.image,this.config):{}:(this.state="run:age",n=ut(),r=this.config.face.age.enabled?await e2(f.image,this.config):{},this.perf.age=Math.trunc(ut()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?i2(f.image,this.config):{}:(this.state="run:gender",n=ut(),a=this.config.face.gender.enabled?await i2(f.image,this.config):{},this.perf.gender=Math.trunc(ut()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?h2(f.image,this.config):{}:(this.state="run:emotion",n=ut(),s=this.config.face.emotion.enabled?await h2(f.image,this.config):{},this.perf.emotion=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?p2(f.image,this.config):[]:(this.state="run:embedding",n=ut(),i=this.config.face.embedding.enabled?await p2(f.image,this.config):[],this.perf.embedding=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((c=f==null?void 0:f.annotations)==null?void 0:c.leftEyeIris)&&((h=f==null?void 0:f.annotations)==null?void 0:h.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let A=((d=f.annotations)==null?void 0:d.leftEyeIris)&&((p=f.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;o.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(m=f.image)==null||m.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async detect(t,n={}){return new Promise(async r=>{var d,p,m,f;this.state="config";let a;this.config=Ic(this.config,n),this.state="check";let s=this.sanity(t);s&&(Te(s,t),r({error:s}));let i,o,l,u=ut();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=ut();let c=y6(t,this.config);if(!c||!c.tensor){Te("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ut()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(c.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ut(),l=this.config.face.enabled?await this.detectFace(c.tensor):[],this.perf.face=Math.trunc(ut()-a)),this.analyze("Start Body:"),this.config.async?(i=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(c.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ut(),i=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(c.tensor,this.config)):[],this.perf.body=Math.trunc(ut()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(m=this.models.handpose)==null?void 0:m.estimateHands(c.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ut(),o=this.config.hand.enabled?await((f=this.models.handpose)==null?void 0:f.estimateHands(c.tensor,this.config)):[],this.perf.hand=Math.trunc(ut()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),c.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ut(),h=[...p6(l),...d6(i),...m6(o),...f6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ut()-a)),this.perf.total=Math.trunc(ut()-u),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:c.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(n0);break;case"full":n=await t(r0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+n0;break;case"full":r=1200,n="data:image/jpeg;base64,"+r0;break;default:n=null}let a=new Image(r,r);a.onload=()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=r,s.height=r;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=i==null?void 0:i.getImageData(0,0,r,r);this.detect(o,this.config).then(l=>t(l))},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(n0):t(r0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s}async warmup(t){let n=ut();t&&(this.config=Ic(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=ut();return this.config.debug&&Te("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};return hae;})(); +2Q==`;var B2={};Zn(B2,{author:()=>R6,browser:()=>E6,bugs:()=>F6,default:()=>yae,dependencies:()=>z6,description:()=>I6,devDependencies:()=>L6,engines:()=>O6,homepage:()=>M6,keywords:()=>B6,license:()=>$6,main:()=>S6,module:()=>T6,name:()=>k6,peerDependencies:()=>P6,repository:()=>D6,scripts:()=>W6,sideEffects:()=>N6,types:()=>C6,version:()=>V2});var k6="@vladmandic/human",V2="0.40.0",I6="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",N6=!1,S6="dist/human.node.js",T6="dist/human.esm.js",E6="dist/human.esm.js",C6="types/human.d.ts",R6="Vladimir Mandic ",F6={url:"https://github.com/vladmandic/human/issues"},M6="https://github.com/vladmandic/human#readme",$6="MIT",O6={node:">=12.0.0"},D6={type:"git",url:"git+https://github.com/vladmandic/human.git"},z6={},P6={},L6={"@tensorflow/tfjs":"^3.2.0","@tensorflow/tfjs-backend-cpu":"^3.2.0","@tensorflow/tfjs-backend-wasm":"^3.2.0","@tensorflow/tfjs-backend-webgl":"^3.2.0","@tensorflow/tfjs-converter":"^3.2.0","@tensorflow/tfjs-core":"^3.2.0","@tensorflow/tfjs-data":"^3.2.0","@tensorflow/tfjs-layers":"^3.2.0","@tensorflow/tfjs-node":"^3.2.0","@tensorflow/tfjs-node-gpu":"^3.2.0","@types/node":"^14.14.31","@typescript-eslint/eslint-plugin":"^4.16.1","@typescript-eslint/parser":"^4.16.1","@vladmandic/pilogger":"^0.2.14",chokidar:"^3.5.1",dayjs:"^1.10.4",esbuild:"^0.8.54",eslint:"^7.21.0","eslint-config-airbnb-base":"^14.2.1","eslint-plugin-import":"^2.22.1","eslint-plugin-json":"^2.1.2","eslint-plugin-node":"^11.1.0","eslint-plugin-promise":"^4.3.1",rimraf:"^3.0.2",seedrandom:"^3.0.5","simple-git":"^2.35.2",tslib:"^2.1.0",typescript:"^4.3.0-dev.20210302"},W6={start:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",lint:"eslint src demo server",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",update:"npm update --depth 20 --force && npm dedupe && npm prune && npm audit"},B6=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],yae={name:k6,version:V2,description:I6,sideEffects:N6,main:S6,module:T6,browser:E6,types:C6,author:R6,bugs:F6,homepage:M6,license:$6,engines:O6,repository:D6,dependencies:z6,peerDependencies:P6,devDependencies:L6,scripts:W6,keywords:B6};var ut=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Nc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Nc(s,i):n[a]=i}),n),{})}var U2=class{constructor(t={}){this.tf=Jc,this.package=B2,this.version=V2,this.config=Nc(v6,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>W2(n,this.config),this.facemesh=V6,this.age=Qy,this.gender=n2,this.emotion=l2,this.body=this.config.body.modelType.startsWith("posenet")?N2:z2,this.hand=M2}profile(){return this.config.profile?Zy:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Se(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?Gv(t,n):0}async load(t=null){this.state="load";let n=ut();t&&(this.config=Nc(this.config,t)),this.firstRun&&(this.config.debug&&Se(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Se("configuration:",this.config),this.config.debug&&Se("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Yy:V6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?e2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?i2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?h2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?p2(this.config):null),this.models.handpose||(this.config.hand.enabled?D2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?T2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?P2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await e2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await i2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await h2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await p2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await D2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await T2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await P2(this.config))),this.firstRun&&(this.config.debug&&Se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(ut()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ut();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Se("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Se("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Se("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Fv();try{await this.tf.setBackend(this.config.backend)}catch(r){Se("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Se("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Se(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ut()-n)}}async detectFace(t){var u,c,h,d,p,m;let n,r,a,s,i,o=[];this.state="run:face",n=ut();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));this.perf.face=Math.trunc(ut()-n);for(let f of l){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Se("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?t2(f.image,this.config):{}:(this.state="run:age",n=ut(),r=this.config.face.age.enabled?await t2(f.image,this.config):{},this.perf.age=Math.trunc(ut()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?o2(f.image,this.config):{}:(this.state="run:gender",n=ut(),a=this.config.face.gender.enabled?await o2(f.image,this.config):{},this.perf.gender=Math.trunc(ut()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?d2(f.image,this.config):{}:(this.state="run:emotion",n=ut(),s=this.config.face.emotion.enabled?await d2(f.image,this.config):{},this.perf.emotion=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?f2(f.image,this.config):[]:(this.state="run:embedding",n=ut(),i=this.config.face.embedding.enabled?await f2(f.image,this.config):[],this.perf.embedding=Math.trunc(ut()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((c=f==null?void 0:f.annotations)==null?void 0:c.leftEyeIris)&&((h=f==null?void 0:f.annotations)==null?void 0:h.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let A=((d=f.annotations)==null?void 0:d.leftEyeIris)&&((p=f.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;o.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(m=f.image)==null||m.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async detect(t,n={}){return new Promise(async r=>{var d,p,m,f;this.state="config";let a;this.config=Nc(this.config,n),this.state="check";let s=this.sanity(t);s&&(Se(s,t),r({error:s}));let i,o,l,u=ut();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=ut();let c=W2(t,this.config);if(!c||!c.tensor){Se("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ut()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(c.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ut(),l=this.config.face.enabled?await this.detectFace(c.tensor):[],this.perf.face=Math.trunc(ut()-a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(c.tensor,this.config):[]:i=this.config.body.enabled?L2(c.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ut(),this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(c.tensor,this.config)):[]:i=this.config.body.enabled?await L2(c.tensor,this.config):[],this.perf.body=Math.trunc(ut()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(m=this.models.handpose)==null?void 0:m.estimateHands(c.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ut(),o=this.config.hand.enabled?await((f=this.models.handpose)==null?void 0:f.estimateHands(c.tensor,this.config)):[],this.perf.hand=Math.trunc(ut()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),c.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ut(),h=[...x6(l),...g6(i),..._6(o),...w6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ut()-a)),this.perf.total=Math.trunc(ut()-u),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:c.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(r0);break;case"full":n=await t(a0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+r0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+a0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(r0):t(a0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s}async warmup(t){let n=ut();t&&(this.config=Nc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=ut();return this.config.debug&&Se("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};return gae;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.ts.map b/dist/human.ts.map index 855f382d..3884832f 100644 --- a/dist/human.ts.map +++ b/dist/human.ts.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/blazeface/facemesh.ts", "../src/posenet/keypoints.ts", "../src/human.ts", "../src/log.ts", "../node_modules/node-fetch/browser.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/seedrandom.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/index.js", 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"../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_to.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ceil.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clip_by_value.ts", 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"../src/handpose/anchors.ts", "../src/gesture/gesture.ts", "../src/imagefx.js", "../src/image.ts", "../config.js", "../src/sample.ts"], - "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length;\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n /*\n async processImage(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process?.tensor?.dispose();\n return process?.canvas;\n }\n */\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n const data = ctx?.getImageData(0, 0, size, size);\n this.detect(data, this.config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var 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___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var _emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=function(){return(_emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=Module[\"asm\"][\"eb\"]).apply(null,arguments)};var _emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=function(){return(_emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=Module[\"asm\"][\"fb\"]).apply(null,arguments)};var _emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModule) {\n WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var 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doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n 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x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detection, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"../node_modules/@tensorflow/tfjs-data/src/datasets/text_line_dataset.ts", "../node_modules/@tensorflow/tfjs-data/src/datasets/csv_dataset.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/microphone_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/webcam_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/datasource.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/string_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/byte_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/file_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/iterators/url_chunk_iterator.ts", "../node_modules/@tensorflow/tfjs-data/src/util/source_util.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/file_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/faceboxes/faceboxes.ts", "../src/profile.ts", "../src/age/age.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/decodePose.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/gesture/gesture.ts", "../src/imagefx.js", "../src/image.ts", "../config.js", "../src/sample.ts"], + "sourcesContent": ["import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n facePipeline: any;\n config: any;\n\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results: Array<{}> = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n boxConfidence: prediction.boxConfidence,\n faceConfidence: prediction.faceConfidence,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config) {\n // @ts-ignore\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.triangulation = coords.TRI468;\n", "export const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints\n\nexport const partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceboxes from './faceboxes/faceboxes';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image';\nimport * as profile from './profile';\nimport * as config from '../config';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\nclass Human {\n tf: any;\n package: any;\n version: string;\n config: any;\n fx: any;\n state: string;\n numTensors: number;\n analyzeMemoryLeaks: boolean;\n checkSanity: boolean;\n firstRun: boolean;\n perf: any;\n image: any;\n models: any;\n // models\n facemesh: any;\n age: any;\n gender: any;\n emotion: any;\n body: any;\n hand: any;\n\n constructor(userConfig = {}) {\n this.tf = tf;\n this.package = app;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n blazepose: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export access to image processing\n this.image = (input) => image.process(input, this.config);\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = this.tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig = null) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n if (this.config.debug) log(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`);\n await this.checkBackend(true);\n if (this.tf.ENV.flags.IS_BROWSER) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n const face = this.config.face.detector.modelPath.includes('faceboxes') ? faceboxes : facemesh;\n if (this.config.async) {\n [\n this.models.face,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.handpose,\n this.models.posenet,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.face || (this.config.face.enabled ? face.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);\n if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n }\n\n if (this.firstRun) {\n if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n this.firstRun = false;\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force = false) {\n if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n if (this.config.backend && this.config.backend !== '') {\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('settings wasm path:', this.config.wasmPath);\n this.tf.setWasmPaths(this.config.wasmPath);\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (this.tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.face?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n faceConfidence: face.faceConfidence,\n boxConfidence: face.boxConfidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n // image: face.image.toInt().squeeze(),\n });\n\n // dont need face anymore\n face.image?.dispose();\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let bodyRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) this.tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run body: can be posenet or blazepose\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) this.tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n // @ts-ignore\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, this.config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-face.jpg');\n const img = new Image();\n img.onload = async () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = img.naturalWidth;\n canvas.height = img.naturalHeight;\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const res = await this.detect(canvas, this.config);\n resolve(res);\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmupNode() {\n const atob = (str) => Buffer.from(str, 'base64');\n const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n // @ts-ignore\n const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n const res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n return res;\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else if (typeof Image !== 'undefined') res = await this.warmupCanvas();\n else res = await this.warmupNode();\n this.config.videoOptimized = video;\n const t1 = now();\n if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var 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asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModule) {\n WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs: packageBundle.version,\n 'tfjs-core': packageCore.version,\n 'tfjs-data': packageData.version,\n 'tfjs-layers': packageLayers.version,\n 'tfjs-converter': packageConverter.version,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n gl: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n log('backend registration:', config.name);\n try {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n // @ts-ignore\n config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n } catch (err) {\n log('error: cannot get WebGL2 context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL2 context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n // @ts-ignore\n // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n log('backend registered:', config.name);\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nexport const disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n blazeFaceModel: any;\n width: number;\n height: number;\n anchorsData: any;\n anchors: any;\n inputSize: number;\n config: any;\n scaleFaces: number;\n\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n}\n\nexport async function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.6) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n // @ts-ignore\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n storedBoxes: any;\n boundingBoxDetector: any;\n meshDetector: any;\n irisModel: any;\n meshWidth: number;\n meshHeight: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const faceConfidence = confidence.dataSync()[0];\n if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n // @ts-ignore\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n rawCoords,\n };\n if (!config.face.mesh.returnRawData) delete prediction.rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nexport class FaceBoxes {\n enlarge: number;\n model: any;\n config: any;\n\n constructor(model, config) {\n this.enlarge = 1.1;\n this.model = model;\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];\n const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const castT = resizeT.toInt();\n let scores;\n let boxes;\n if (!config.profile) {\n const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);\n scores = scoresT.dataSync();\n const squeezeT = boxesT.squeeze();\n boxes = squeezeT.arraySync();\n scoresT.dispose();\n boxesT.dispose();\n squeezeT.dispose();\n numT.dispose();\n } else {\n const profileData = await tf.profile(() => this.model.executeAsync(castT));\n scores = profileData.result[0].dataSync();\n const squeezeT = profileData.result[1].squeeze();\n boxes = squeezeT.arraySync();\n profileData.result.forEach((t) => t.dispose());\n profile.run('faceboxes', profileData);\n }\n castT.dispose();\n resizeT.dispose();\n for (const i in boxes) {\n if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {\n const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];\n const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];\n const box = [\n parseInt((boxRaw[0] * input.shape[2]).toString()),\n parseInt((boxRaw[1] * input.shape[1]).toString()),\n parseInt((boxRaw[2] * input.shape[2]).toString()),\n parseInt((boxRaw[3] * input.shape[1]).toString())];\n const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);\n const image = resized.div([255]);\n resized.dispose();\n results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });\n // add mesh, meshRaw, annotations,\n }\n }\n return results;\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.face.detector.modelPath);\n if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n const faceboxes = new FaceBoxes(model, config);\n if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceboxes;\n}\n", "import { log } from './log';\n\nexport const data = {};\n\nexport function run(name: string, raw: any) {\n if (!raw || !raw.kernels) return;\n const maxResults = 5;\n const time = raw.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = raw.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n data[name] = res;\n log('Human profiler', name, res);\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.age.modelPath);\n if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = { age: 0 };\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await model.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = model.inputs[0].shape[3] === 1;\n if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = { gender: '', confidence: 0 };\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await model.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.emotion.modelPath);\n if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await model.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nlet model;\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.face.embedding.modelPath);\n if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport function simmilarity(embedding1, embedding2) {\n if (!embedding1 || !embedding2) return 0;\n if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data: Array<[]> = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await model.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nexport class PoseNet {\n baseModel: any;\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\n\nexport async function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n const mobilenet = new modelBase.BaseModel(model);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n model: any;\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nexport class MaxHeap {\n priorityQueue: any;\n numberOfElements: number;\n getElementValue: any;\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result: Array = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nexport function argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses: Array<{ keypoints: any, score: number }> = [];\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nexport class HandPose {\n handPipeline: any;\n\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });\n }\n return hands;\n }\n}\n\nexport async function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n model: any;\n anchors: any;\n anchorsTensor: any;\n inputSizeTensor: any;\n doubleInputSizeTensor: any;\n\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands: Array<{}> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\n", "export function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n handDetector: any;\n landmarkDetector: any;\n inputSize: number;\n storedBoxes: any;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{}> = [];\n\n if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n", "export const anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n 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x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "import { log } from '../log';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nconst labels = [\n 'nose',\n 'leftEyeInside',\n 'leftEye',\n 'leftEyeOutside',\n 'rightEyeInside',\n 'rightEye',\n 'rightEyeOutside',\n 'leftEar',\n 'rightEar',\n 'leftMouth',\n 'rightMouth',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftPalm',\n 'rightPalm',\n 'leftIndex',\n 'rightIndex',\n 'leftPinky',\n 'rightPinky',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n 'leftHeel',\n 'rightHeel',\n 'leftFoot',\n 'rightFoot',\n 'midHip',\n 'forehead',\n 'leftThumb',\n 'leftHand',\n 'rightThumb',\n 'rightHand',\n];\n\nexport async function load(config) {\n if (!model) {\n model = await tf.loadGraphModel(config.body.modelPath);\n model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return model;\n}\n\nexport async function predict(image, config) {\n if (!model) return null;\n if (!config.body.enabled) return null;\n const imgSize = { width: image.shape[2], height: image.shape[1] };\n const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);\n const normalize = tf.div(resize, [255.0]);\n resize.dispose();\n // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides\n // let poseflag; // irrelevant\n let points;\n if (!config.profile) {\n const resT = await model.predict(normalize);\n // segmentation = resT[0].dataSync();\n // poseflag = resT[1].dataSync();\n points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n resT.forEach((t) => t.dispose());\n } else {\n const profileData = await tf.profile(() => model.predict(normalize));\n // segmentation = profileData.result[0].dataSync();\n // poseflag = profileData.result[1].dataSync();\n points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties\n profileData.result.forEach((t) => t.dispose());\n profile.run('blazepose', profileData);\n }\n normalize.dispose();\n const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n for (let i = 0; i < points.length / 5; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: {\n x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),\n y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),\n z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero\n },\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n // console.log('POINTS', imgSize, pts.length, pts);\n return [{ keypoints }];\n}\n\n/*\nModel card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view\nDownload: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints\n*/\n", "export const body = (res) => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexport const face = (res) => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res) => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexport const hand = (res) => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: string }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: number }> = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n // @ts-ignore\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _filter = {};\n const _canvas = params.canvas || document.createElement('canvas');\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n const DRAW = { INTERMEDIATE: 1 };\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, _width, _height);\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _draw = function (flags = null) {\n let source = null;\n let target = null;\n let flipY = false;\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n return _canvas;\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n // Compile shaders\n const SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n const program = _compileShader(_filter.blur.SHADER);\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n const program = _compileShader(_filter.pixelate.SHADER);\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from './log';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n // log('created FX filter');\n fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!fx) return inCanvas;\n fx.reset();\n fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) fx.addFilter('negative');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.vintage) fx.addFilter('brownie');\n if (config.filter.sepia) fx.addFilter('sepia');\n if (config.filter.kodachrome) fx.addFilter('kodachrome');\n if (config.filter.technicolor) fx.addFilter('technicolor');\n if (config.filter.polaroid) fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n // leave as empty string to continue using default backend\n // when backend is set outside of Human library\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'blazeface-front', 'blazeface-back' or 'faceboxes'\n // 'blazeface-front' is blazeface model optimized for large faces such as front-facing camera\n // 'blazeface-back' is blazeface model optimized for smaller and/or distanct faces\n // 'faceboxes' is alternative model to 'blazeface'\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json',\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion.json',\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as blazepose only detects single pose\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n // only valid for posenet as blazepose only detects single pose\n modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n skipInitial: false, // if previous detection resulted in no faces detected,\n // should skipFrames be reset immediately\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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- } - ] -} \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 9ed7b162..c1401868 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,6 +1,6 @@ { "name": "@vladmandic/human", - "version": "0.30.6", + "version": "0.40.0", "lockfileVersion": 1, "requires": true, "dependencies": { diff --git a/package.json b/package.json index 5d63fadf..328b4740 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "@vladmandic/human", - "version": "0.30.6", + "version": "0.40.0", "description": "Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition", "sideEffects": false, "main": "dist/human.node.js", @@ -71,6 +71,8 @@ "age-estimation", "emotion-detection", "gender-prediction", - "gesture-recognition" + "gesture-recognition", + "blazeface", + "blazepose" ] } diff --git a/src/blazepose/blazepose.ts b/src/blazepose/blazepose.ts new file mode 100644 index 00000000..2b1c03a8 --- /dev/null +++ b/src/blazepose/blazepose.ts @@ -0,0 +1,104 @@ +import { log } from '../log'; +import * as tf from '../../dist/tfjs.esm.js'; +import * as profile from '../profile'; + +let model; +const labels = [ + 'nose', + 'leftEyeInside', + 'leftEye', + 'leftEyeOutside', + 'rightEyeInside', + 'rightEye', + 'rightEyeOutside', + 'leftEar', + 'rightEar', + 'leftMouth', + 'rightMouth', + 'leftShoulder', + 'rightShoulder', + 'leftElbow', + 'rightElbow', + 'leftWrist', + 'rightWrist', + 'leftPalm', + 'rightPalm', + 'leftIndex', + 'rightIndex', + 'leftPinky', + 'rightPinky', + 'leftHip', + 'rightHip', + 'leftKnee', + 'rightKnee', + 'leftAnkle', + 'rightAnkle', + 'leftHeel', + 'rightHeel', + 'leftFoot', + 'rightFoot', + 'midHip', + 'forehead', + 'leftThumb', + 'leftHand', + 'rightThumb', + 'rightHand', +]; + +export async function load(config) { + if (!model) { + model = await tf.loadGraphModel(config.body.modelPath); + model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size); + model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size); + if (config.debug) log(`load model: ${config.body.modelPath.match(/\/(.*)\./)[1]}`); + } + return model; +} + +export async function predict(image, config) { + if (!model) return null; + if (!config.body.enabled) return null; + const imgSize = { width: image.shape[2], height: image.shape[1] }; + const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false); + const normalize = tf.div(resize, [255.0]); + resize.dispose(); + // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides + // let poseflag; // irrelevant + let points; + if (!config.profile) { + const resT = await model.predict(normalize); + // segmentation = resT[0].dataSync(); + // poseflag = resT[1].dataSync(); + points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); + resT.forEach((t) => t.dispose()); + } else { + const profileData = await tf.profile(() => model.predict(normalize)); + // segmentation = profileData.result[0].dataSync(); + // poseflag = profileData.result[1].dataSync(); + points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties + profileData.result.forEach((t) => t.dispose()); + profile.run('blazepose', profileData); + } + normalize.dispose(); + const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = []; + for (let i = 0; i < points.length / 5; i++) { + keypoints.push({ + id: i, + part: labels[i], + position: { + x: Math.trunc(imgSize.width * points[5 * i + 0] / 255), + y: Math.trunc(imgSize.height * points[5 * i + 1] / 255), + z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero + }, + score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value + presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value + }); + } + // console.log('POINTS', imgSize, pts.length, pts); + return [{ keypoints }]; +} + +/* +Model card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view +Download: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints +*/ diff --git a/src/human.ts b/src/human.ts index 165b344b..f28523ee 100644 --- a/src/human.ts +++ b/src/human.ts @@ -9,6 +9,7 @@ import * as emotion from './emotion/emotion'; import * as embedding from './embedding/embedding'; import * as posenet from './posenet/posenet'; import * as handpose from './handpose/handpose'; +import * as blazepose from './blazepose/blazepose'; import * as gesture from './gesture/gesture'; import * as image from './image'; import * as profile from './profile'; @@ -49,6 +50,7 @@ class Human { checkSanity: boolean; firstRun: boolean; perf: any; + image: any; models: any; // models facemesh: any; @@ -74,18 +76,21 @@ class Human { this.models = { facemesh: null, posenet: null, + blazepose: null, handpose: null, iris: null, age: null, gender: null, emotion: null, }; + // export access to image processing + this.image = (input) => image.process(input, this.config); // export raw access to underlying models this.facemesh = facemesh; this.age = age; this.gender = gender; this.emotion = emotion; - this.body = posenet; + this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose; this.hand = handpose; } @@ -146,16 +151,18 @@ class Human { this.models.gender, this.models.emotion, this.models.embedding, - this.models.posenet, this.models.handpose, + this.models.posenet, + this.models.blazepose, ] = await Promise.all([ this.models.face || (this.config.face.enabled ? face.load(this.config) : null), this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null), this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null), this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null), this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null), - this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null), this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null), + this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null), + this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null), ]); } else { if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config); @@ -163,8 +170,9 @@ class Human { if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config); if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config); if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config); - if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config); if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config); + if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config); + if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config); } if (this.firstRun) { @@ -346,16 +354,6 @@ class Human { return faceRes; } - /* - async processImage(input, userConfig = {}) { - this.state = 'image'; - this.config = mergeDeep(this.config, userConfig); - const process = image.process(input, this.config); - process?.tensor?.dispose(); - return process?.canvas; - } - */ - // main detect function async detect(input, userConfig = {}) { // detection happens inside a promise @@ -374,7 +372,7 @@ class Human { resolve({ error }); } - let poseRes; + let bodyRes; let handRes; let faceRes; @@ -410,15 +408,17 @@ class Human { this.perf.face = Math.trunc(now() - timeStamp); } - // run posenet + // run body: can be posenet or blazepose this.analyze('Start Body:'); if (this.config.async) { - poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : []; + if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : []; + else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : []; if (this.perf.body) delete this.perf.body; } else { this.state = 'run:body'; timeStamp = now(); - poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : []; + if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : []; + else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : []; this.perf.body = Math.trunc(now() - timeStamp); } this.analyze('End Body:'); @@ -438,7 +438,7 @@ class Human { // if async wait for results if (this.config.async) { - [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]); + [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]); } process.tensor.dispose(); @@ -449,14 +449,14 @@ class Human { if (this.config.gesture.enabled) { timeStamp = now(); // @ts-ignore - gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)]; + gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)]; if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp); else if (this.perf.gesture) delete this.perf.gesture; } this.perf.total = Math.trunc(now() - timeStart); this.state = 'idle'; - resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas }); + resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas }); }); } @@ -487,21 +487,24 @@ class Human { src = 'data:image/jpeg;base64,' + sample.face; break; case 'full': + case 'body': size = 1200; src = 'data:image/jpeg;base64,' + sample.body; break; default: src = null; } - const img = new Image(size, size); - img.onload = () => { + // src = encodeURI('../assets/human-sample-face.jpg'); + const img = new Image(); + img.onload = async () => { const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas'); - canvas.width = size; - canvas.height = size; + canvas.width = img.naturalWidth; + canvas.height = img.naturalHeight; const ctx = canvas.getContext('2d'); ctx?.drawImage(img, 0, 0); - const data = ctx?.getImageData(0, 0, size, size); - this.detect(data, this.config).then((res) => resolve(res)); + // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width); + const res = await this.detect(canvas, this.config); + resolve(res); }; if (src) img.src = src; else resolve(null); diff --git a/src/node.js b/src/node.js index 55493d7b..56be274f 100644 --- a/src/node.js +++ b/src/node.js @@ -32,7 +32,7 @@ const config = { iris: { modelPath: 'file://models/iris.json' }, age: { modelPath: 'file://models/age-ssrnet-imdb.json' }, gender: { modelPath: 'file://models/gender.json' }, - emotion: { modelPath: 'file://models/emotion-large.json' }, + emotion: { modelPath: 'file://models/emotion.json' }, embedding: { modelPath: 'file://models/mobilefacenet.json' }, }, body: { modelPath: 'file://models/posenet.json' }, diff --git a/src/posenet/decodeMultiple.ts b/src/posenet/decodeMultiple.ts index 8a2e054f..44ea8746 100644 --- a/src/posenet/decodeMultiple.ts +++ b/src/posenet/decodeMultiple.ts @@ -3,6 +3,7 @@ import * as decodePose from './decodePose'; import * as vectors from './vectors'; const kLocalMaximumRadius = 1; +const defaultOutputStride = 16; function withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) { return poses.some(({ keypoints }) => { @@ -28,10 +29,10 @@ export function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFw // The top element in the queue is the next root candidate. const root = queue.dequeue(); // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance. - const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer); + const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer); if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue; // Else start a new detection instance at the position of the root. - const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer); + const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer); const score = getInstanceScore(poses, squaredNmsRadius, keypoints); if (score > config.body.scoreThreshold) poses.push({ keypoints, score }); } diff --git a/src/posenet/decodePose.ts b/src/posenet/decodePose.ts index f54e1e1d..c94dda57 100644 --- a/src/posenet/decodePose.ts +++ b/src/posenet/decodePose.ts @@ -5,6 +5,9 @@ import * as decoders from './decoders'; const parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]])); const parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId); const childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId); + +const defaultOutputStride = 16; + function getDisplacement(edgeId, point, displacements) { const numEdges = displacements.shape[2] / 2; return { @@ -12,6 +15,7 @@ function getDisplacement(edgeId, point, displacements) { x: displacements.get(point.y, point.x, numEdges + edgeId), }; } + function getStridedIndexNearPoint(point, outputStride, height, width) { return { y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1), @@ -77,7 +81,7 @@ export async function decodeSinglePose(heatmapScores, offsets, config) { const scoresBuffer = allTensorBuffers[0]; const offsetsBuffer = allTensorBuffers[1]; const heatmapValuesBuffer = allTensorBuffers[2]; - const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer); + const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer); const offsetPointsBuffer = await offsetPoints.buffer(); const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer)); const instanceKeypoints = keypointConfidence.map((score, i) => { diff --git a/src/posenet/keypoints.ts b/src/posenet/keypoints.ts index ee6a6a8c..424a1a68 100644 --- a/src/posenet/keypoints.ts +++ b/src/posenet/keypoints.ts @@ -4,7 +4,7 @@ export const partNames = [ 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle', ]; -export const NUM_KEYPOINTS = exports.partNames.length; +export const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints export const partIds = exports.partNames.reduce((result, jointName, i) => { result[jointName] = i; diff --git a/src/posenet/modelBase.ts b/src/posenet/modelBase.ts index 3570080b..12c5dd61 100644 --- a/src/posenet/modelBase.ts +++ b/src/posenet/modelBase.ts @@ -20,11 +20,11 @@ export class BaseModel { predict(input, config) { return tf.tidy(() => { - const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0); + const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0); const asBatch = asFloat.expandDims(0); const results = this.model.predict(asBatch); const results3d = results.map((y) => y.squeeze([0])); - const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d); + const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d); return { heatmapScores: namedResults.heatmap.sigmoid(), offsets: namedResults.offsets, diff --git a/types/blazepose/blazepose.d.ts b/types/blazepose/blazepose.d.ts new file mode 100644 index 00000000..4f33b5d0 --- /dev/null +++ b/types/blazepose/blazepose.d.ts @@ -0,0 +1,14 @@ +export declare function load(config: any): Promise; +export declare function predict(image: any, config: any): Promise<{ + keypoints: { + id: any; + part: any; + position: { + x; + y; + z; + }; + score: any; + presence: any; + }[]; +}[] | null>; diff --git a/types/human.d.ts b/types/human.d.ts index 55c4b745..c7c8d554 100644 --- a/types/human.d.ts +++ b/types/human.d.ts @@ -10,6 +10,7 @@ declare class Human { checkSanity: boolean; firstRun: boolean; perf: any; + image: any; models: any; facemesh: any; age: any; diff --git a/wiki b/wiki index 55e854ea..c60f4427 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 55e854ea9263ca0eae7ffbb7d60b87e1ca3a7065 +Subproject commit c60f442714b1b5887ae25feb35fa413bc9996402