From 38714b023f3c16408fe54da904c23a770a05f406 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 14 Aug 2021 11:16:26 -0400 Subject: [PATCH] complete async work --- demo/index.js | 10 +- dist/human.esm-nobundle.js | 11055 ++++- dist/human.esm-nobundle.js.map | 4 +- dist/human.esm.js | 69032 +++++++++++++++++++++++++- dist/human.esm.js.map | 4 +- dist/human.js | 69060 ++++++++++++++++++++++++++- dist/human.node-gpu.js | 213 +- dist/human.node-wasm.js | 213 +- dist/human.node.js | 213 +- package.json | 2 +- src/blazeface/blazeface.ts | 6 +- src/blazeface/facepipeline.ts | 179 +- src/efficientpose/efficientpose.ts | 12 +- src/embedding/embedding.ts | 8 +- src/faceres/faceres.ts | 30 +- src/gear/agegenderrace.ts | 6 +- src/gender/gender.ts | 6 +- src/handpose/handpipeline.ts | 2 +- src/human.ts | 21 +- 19 files changed, 146403 insertions(+), 3673 deletions(-) diff --git a/demo/index.js b/demo/index.js index e0e3b465..8ffa3d40 100644 --- a/demo/index.js +++ b/demo/index.js @@ -41,9 +41,9 @@ let userConfig = { flip: false, }, face: { enabled: true, - detector: { return: false }, + detector: { return: false, rotation: true }, mesh: { enabled: true }, - iris: { enabled: false }, + iris: { enabled: true }, description: { enabled: false }, emotion: { enabled: false }, }, @@ -441,9 +441,9 @@ function webWorker(input, image, canvas, timestamp) { // main processing function when input is webcam, can use direct invocation or web worker function runHumanDetect(input, canvas, timestamp) { // if live video - const videoLive = (input.readyState > 2) && (!input.paused); - const cameraLive = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live'); - const live = videoLive || cameraLive; + const videoLive = input.readyState > 2; + const cameraLive = input.srcObject?.getVideoTracks()[0].readyState === 'live'; + const live = (videoLive || cameraLive) && (!input.paused); if (!live) { // stop ui refresh // if (ui.drawThread) cancelAnimationFrame(ui.drawThread); diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index ac4c39dc..ed5a6bc8 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -4,14 +4,10581 @@ homepage: author: ' */ -var a5=Object.defineProperty;var fe=Object.getOwnPropertyDescriptor;var me=Object.getOwnPropertyNames;var he=Object.prototype.hasOwnProperty;var ue=A=>a5(A,"__esModule",{value:!0});var iA=(A,e)=>{ue(A);for(var t in e)a5(A,t,{get:e[t],enumerable:!0})},p=(A,e,t)=>{if(e&&typeof e=="object"||typeof e=="function")for(let r of me(e))!he.call(A,r)&&r!=="default"&&a5(A,r,{get:()=>e[r],enumerable:!(t=fe(e,r))||t.enumerable});return A};var xA=(A,e,t)=>{if(!e.has(A))throw TypeError("Cannot "+t)};var C=(A,e,t)=>(xA(A,e,"read from private field"),t?t.call(A):e.get(A)),K=(A,e,t)=>{if(e.has(A))throw TypeError("Cannot add the same private member more than once");e instanceof WeakSet?e.add(A):e.set(A,t)},_=(A,e,t,r)=>(xA(A,e,"write to private 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ke=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Ie=[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],Ne=[33,133,362,263,1,78,308],L2=ke.map(A=>E0[A]),Z2=Ie.map(A=>E0[A]),V2=Ne.map(A=>E0[A]);var l5=t0.leftEyeLower0,d5=t0.rightEyeLower0,u0={leftBounds:[l5[0],l5[l5.length-1]],rightBounds:[d5[0],d5[d5.length-1]]},V0={count:468,mouth:13,symmetryLine:[13,t0.midwayBetweenEyes[0]]},PA={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},p0={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function X0(A,e,t,r){for(let n=0;n[i[0]/this.meshSize*(d[0]-this.meshSize/2),i[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),s=r!==0?Z0(r,[0,0]):L0,y=r!==0?a.map(d=>[...pA(d,s),d[2]]):a,x=r!==0?uA(n):L0,l=[...m0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return y.map(d=>[Math.round(d[0]+r0(l,x[0])),Math.round(d[1]+r0(l,x[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(e){let t=e[u0.leftBounds[0]][2],r=e[u0.rightBounds[0]][2];return t-r}getEyeBox(e,t,r,n,i=!1){let a=H0(O0(i5([e[r],e[n]]),this.irisEnlarge)),s=z0(a),y=o.image.cropAndResize(t,[[a.startPoint[1]/this.meshSize,a.startPoint[0]/this.meshSize,a.endPoint[1]/this.meshSize,a.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return i&&o.ENV.flags.IS_BROWSER&&(y=o.image.flipLeftRight(y)),{box:a,boxSize:s,crop:y}}getEyeCoords(e,t,r,n=!1){let i=[];for(let a=0;a{let x=a;return y===2?x=n:y===4&&(x=i),[s[0],s[1],x]})}async predict(e,t){let r=!1,n;if((this.skipped===0||this.skipped>t.face.detector.skipFrames||!t.face.mesh.enabled||!t.skipFrame)&&(n=await this.boundingBoxDetector.getBoundingBoxes(e,t),this.skipped=0),t.skipFrame&&this.skipped++,!t.skipFrame||n&&n.boxes&&(!t.face.mesh.enabled||n.boxes.length!==this.detectedFaces&&this.detectedFaces!==t.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let a of n.boxes){let s=await a.box.startPoint.data(),y=await a.box.endPoint.data(),x=await a.landmarks.array();this.storedBoxes.push({startPoint:s,endPoint:y,landmarks:x,confidence:a.confidence})}this.storedBoxes.length>0&&(r=!0)}if(r){if(!n||!n.boxes||n.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let a=0;a{o.dispose(a.box.startPoint),o.dispose(a.box.endPoint),o.dispose(a.landmarks)});let i=o.tidy(()=>this.storedBoxes.map((a,s)=>{let 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Promise.all(t.map(a=>a.buffer()));for(let a of t)o.dispose(a);let n=await LA(r[0],r[1],r[2],r[3],e.body.maxDetected,e.body.minConfidence);return J.inputs[0].shape?IA(n,[A.shape[1],A.shape[2]],[J.inputs[0].shape[2],J.inputs[0].shape[1]]):[]}async function W5(A){return J?A.debug&&M("cached model:",J.modelUrl):(J=await o.loadGraphModel(L(A.modelBasePath,A.body.modelPath)),!J||!J.modelUrl?M("load model failed:",A.body.modelPath):A.debug&&M("load model:",J.modelUrl)),J}function B0(A){return[Math.abs(A.endPoint[0]-A.startPoint[0]),Math.abs(A.endPoint[1]-A.startPoint[1])]}function w0(A){return[A.startPoint[0]+(A.endPoint[0]-A.startPoint[0])/2,A.startPoint[1]+(A.endPoint[1]-A.startPoint[1])/2]}function ZA(A,e,t){let r=e.shape[1],n=e.shape[2],i=[[A.startPoint[1]/r,A.startPoint[0]/n,A.endPoint[1]/r,A.endPoint[0]/n]];return o.image.cropAndResize(e,i,[0],t)}function VA(A,e){let t=[A.startPoint[0]*e[0],A.startPoint[1]*e[1]],r=[A.endPoint[0]*e[0],A.endPoint[1]*e[1]],n=A.palmLandmarks.map(i=>[i[0]*e[0],i[1]*e[1]]);return{startPoint:t,endPoint:r,palmLandmarks:n,confidence:A.confidence}}function G0(A,e=1.5){let t=w0(A),r=B0(A),n=[e*r[0]/2,e*r[1]/2],i=[t[0]-n[0],t[1]-n[1]],a=[t[0]+n[0],t[1]+n[1]];return{startPoint:i,endPoint:a,palmLandmarks:A.palmLandmarks}}function U0(A){let e=w0(A),t=B0(A),n=Math.max(...t)/2,i=[e[0]-n,e[1]-n],a=[e[0]+n,e[1]+n];return{startPoint:i,endPoint:a,palmLandmarks:A.palmLandmarks}}var 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i.data(),s=o.slice(n,[0,1],[-1,4]),y=this.normalizeBoxes(s);o.dispose(s);let x=await o.image.nonMaxSuppressionAsync(y,a,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence),l=await x.array();o.dispose(i),o.dispose(x);let d=[];for(let f of l)if(a[f]>=t.hand.minConfidence){let h=o.slice(y,[f,0],[1,-1]),v=o.slice(n,[f,5],[1,14]),c=o.tidy(()=>o.reshape(this.normalizeLandmarks(v,f),[-1,2]));o.dispose(v),d.push({box:h,palmLandmarks:c,confidence:a[f]})}return o.dispose(n),o.dispose(y),d}async estimateHandBounds(e,t){let r=e.shape[1],n=e.shape[2],i=o.tidy(()=>o.sub(o.div(o.image.resizeBilinear(e,[this.inputSize,this.inputSize]),127.5),1)),a=await this.getBoxes(i,t);o.dispose(i);let s=[];if(!a||a.length===0)return s;for(let y of a){let x=await y.box.data(),l=x.slice(0,2),d=x.slice(2,4),f=await y.palmLandmarks.array();o.dispose(y.box),o.dispose(y.palmLandmarks),s.push(VA({startPoint:l,endPoint:d,palmLandmarks:f,confidence:y.confidence},[n/this.inputSize,r/this.inputSize]))}return s}};function Be(A){return A-2*Math.PI*Math.floor((A+Math.PI)/(2*Math.PI))}function FA(A,e){let t=Math.PI/2-Math.atan2(-(e[1]-A[1]),e[0]-A[0]);return Be(t)}var CA=(A,e)=>[[1,0,A],[0,1,e],[0,0,1]];function s0(A,e){let t=0;for(let r=0;ra[0]),r=e.map(a=>a[1]),n=[Math.min(...t),Math.min(...r)],i=[Math.max(...t),Math.max(...r)];return{startPoint:n,endPoint:i}}getBoxForPalmLandmarks(e,t){let r=e.map(i=>N5([...i,1],t)),n=this.calculateLandmarksBoundingBox(r);return G0(U0(n),Ue)}getBoxForHandLandmarks(e){let t=this.calculateLandmarksBoundingBox(e),r=G0(U0(t),GA);r.palmLandmarks=[];for(let n=0;n[a[0]*(h[0]-this.inputSize/2),a[1]*(h[1]-this.inputSize/2),a[2]*h[2]]),y=I5(r,[0,0]),x=s.map(h=>[...N5(h,y),h[2]]),l=BA(n),d=[...w0(t),1],f=[s0(d,l[0]),s0(d,l[1])];return x.map(h=>[Math.trunc(h[0]+f[0]),Math.trunc(h[1]+f[1]),Math.trunc(h[2])])}async estimateHands(e,t){let r=!1,n;(this.skipped===0||this.skipped>t.hand.skipFrames||!t.hand.landmarks||!t.skipFrame)&&(n=await 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H5(A,e){let t=await YA.estimateHands(A,e);if(!t)return[];let r=[];for(let n=0;nt[n].landmarks[l]);let a=t[n].landmarks,s=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],y=[0,0,0,0];if(a&&a.length>0){for(let x of a)x[0]s[2]&&(s[2]=x[0]),x[1]>s[3]&&(s[3]=x[1]);s[2]-=s[0],s[3]-=s[1],y=[s[0]/(A.shape[2]||0),s[1]/(A.shape[1]||0),s[2]/(A.shape[2]||0),s[3]/(A.shape[1]||0)]}else s=t[n].box?[Math.trunc(Math.max(0,t[n].box.topLeft[0])),Math.trunc(Math.max(0,t[n].box.topLeft[1])),Math.trunc(Math.min(A.shape[2]||0,t[n].box.bottomRight[0])-Math.max(0,t[n].box.topLeft[0])),Math.trunc(Math.min(A.shape[1]||0,t[n].box.bottomRight[1])-Math.max(0,t[n].box.topLeft[1]))]:[0,0,0,0],y=[t[n].box.topLeft[0]/(A.shape[2]||0),t[n].box.topLeft[1]/(A.shape[1]||0),(t[n].box.bottomRight[0]-t[n].box.topLeft[0])/(A.shape[2]||0),(t[n].box.bottomRight[1]-t[n].box.topLeft[1])/(A.shape[1]||0)];r.push({id:n,score:Math.round(100*t[n].confidence)/100,box:s,boxRaw:y,keypoints:a,annotations:i})}return r}async function L5(A){!a0||!i0?([a0,i0]=await Promise.all([A.hand.enabled?o.loadGraphModel(L(A.modelBasePath,A.hand.detector.modelPath),{fromTFHub:A.hand.detector.modelPath.includes("tfhub.dev")}):null,A.hand.landmarks?o.loadGraphModel(L(A.modelBasePath,A.hand.skeleton.modelPath),{fromTFHub:A.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),A.hand.enabled&&(!a0||!a0.modelUrl?M("load model failed:",A.hand.detector.modelPath):A.debug&&M("load model:",a0.modelUrl),!i0||!i0.modelUrl?M("load model failed:",A.hand.skeleton.modelPath):A.debug&&M("load model:",i0.modelUrl))):(A.debug&&M("cached model:",a0.modelUrl),A.debug&&M("cached model:",i0.modelUrl));let e=new k5(a0);return YA=new O5(e,i0),[a0,i0]}var DA=["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"],KA=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var G;async function J0(A){return G?A.debug&&M("cached model:",G.modelUrl):(G=await o.loadGraphModel(L(A.modelBasePath,A.body.modelPath)),G.width=parseInt(G.signature.inputs["input_1:0"].tensorShape.dim[2].size),G.height=parseInt(G.signature.inputs["input_1:0"].tensorShape.dim[1].size),!G||!G.modelUrl?M("load model failed:",A.body.modelPath):A.debug&&M("load model:",G.modelUrl)),G}async function Z5(A,e){if(!G)return[];if(!e.body.enabled)return[];let t={width:A.shape[2]||0,height:A.shape[1]||0},r=o.image.resizeBilinear(A,[G.width,G.height],!1),n=o.div(r,[255]);o.dispose(r);let i=await G.predict(n),a=i.find(b=>b.size===195||b.size===155),s=await(a==null?void 0:a.data())||[];i.forEach(b=>o.dispose(b)),o.dispose(n);let y=[],x=(s==null?void 0:s.length)===195?DA:KA,l=5;for(let b=0;bb.position[0]),f=y.map(b=>b.position[1]),h=[Math.min(...d),Math.min(...f),Math.max(...d)-Math.min(...d),Math.max(...f)-Math.min(...d)],v=[0,0,0,0],c=y.reduce((b,g)=>g.score>b?g.score:b,0);return[{id:0,score:c,box:h,boxRaw:v,keypoints:y}]}var U,o0=[],V5=[0,0,0,0],X5=[0,0,0,0],Y0=0,F5=Number.MAX_SAFE_INTEGER,De=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function QA(A){return U?A.debug&&M("cached model:",U.modelUrl):(U=await o.loadGraphModel(L(A.modelBasePath,A.body.modelPath)),!U||!U.modelUrl?M("load model failed:",A.body.modelPath):A.debug&&M("load model:",U.modelUrl)),U}function Ke(A,e){let[t,r]=A.shape;return o.tidy(()=>{let n=(s,y)=>o.sub(s,o.mul(o.div(s,o.scalar(y,"int32")),o.scalar(y,"int32"))),i=o.reshape(A,[r*t]),a=o.max(i,0).dataSync()[0];if(a>e){let s=o.argMax(i,0),y=n(s,t).dataSync()[0],x=o.div(s,o.scalar(t,"int32")).dataSync()[0];return[y,x,a]}return[0,0,a]})}async function C5(A,e){return F50?(F5++,[{id:0,score:Y0,box:V5,boxRaw:X5,keypoints:o0}]):(F5=0,new Promise(async t=>{let r=o.tidy(()=>{if(!U.inputs[0].shape)return null;let 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e0,n0=[],q5=[0,0,0,0],B5=[0,0,0,0],g0=0,G5=Number.MAX_SAFE_INTEGER,Qe=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function U5(A){return e0?A.debug&&M("cached model:",e0.modelUrl):(e0=await o.loadGraphModel(L(A.modelBasePath,A.body.modelPath)),!e0||!e0.modelUrl?M("load model failed:",A.body.modelPath):A.debug&&M("load model:",e0.modelUrl)),e0}async function J5(A,e){return G50?(G5++,[{id:0,score:g0,box:q5,boxRaw:B5,keypoints:n0}]):(G5=0,new Promise(async t=>{let r=o.tidy(()=>{if(!e0.inputs[0].shape)return null;let x=o.image.resizeBilinear(A,[e0.inputs[0].shape[2],e0.inputs[0].shape[1]],!1);return o.cast(x,"int32")}),n;if(e.body.enabled&&(n=await e0.predict(r)),o.dispose(r),n){n0.length=0;let x=await n.array();o.dispose(n);let l=x[0][0];for(let 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a=0;ax.part==="leftShoulder"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightShoulder"),s&&y.push([s.position[0],s.position[1]]),W0(n,y,r),y.length=0,s=e[a].keypoints.find(x=>x.part==="rightShoulder"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightHip"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftHip"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftShoulder"),s&&y.push([s.position[0],s.position[1]]),y.length===4&&sA(n,y,r),y.length=0,s=e[a].keypoints.find(x=>x.part==="leftHip"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftKnee"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftAnkle"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftHeel"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftFoot"),s&&y.push([s.position[0],s.position[1]]),W0(n,y,r),y.length=0,s=e[a].keypoints.find(x=>x.part==="rightHip"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightKnee"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightAnkle"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightHeel"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightFoot"),s&&y.push([s.position[0],s.position[1]]),W0(n,y,r),y.length=0,s=e[a].keypoints.find(x=>x.part==="leftShoulder"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftElbow"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftWrist"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="leftPalm"),s&&y.push([s.position[0],s.position[1]]),W0(n,y,r),y.length=0,s=e[a].keypoints.find(x=>x.part==="rightShoulder"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightElbow"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightWrist"),s&&y.push([s.position[0],s.position[1]]),s=e[a].keypoints.find(x=>x.part==="rightPalm"),s&&y.push([s.position[0],s.position[1]]),W0(n,y,r)}}}}async function ie(A,e,t){let r=q(x0,t);if(!e||!A||!(A instanceof HTMLCanvasElement))return;let n=A.getContext("2d");if(!!n){n.lineJoin="round",n.font=r.font;for(let i of e){if(r.drawBoxes&&(n.strokeStyle=r.color,n.fillStyle=r.color,S0(n,i.box[0],i.box[1],i.box[2],i.box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(n.fillStyle=r.shadowColor,n.fillText("hand",i.box[0]+3,1+i.box[1]+r.lineHeight,i.box[2])),n.fillStyle=r.labelColor,n.fillText("hand",i.box[0]+2,0+i.box[1]+r.lineHeight,i.box[2])),n.stroke()),r.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let a of i.keypoints)n.fillStyle=r.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:r.color,rA(n,a[0],a[1],0,r);if(r.drawLabels){let a=(s,y)=>{n.fillStyle=r.useDepth?`rgba(${127.5+2*s[s.length-1][2]}, ${127.5-2*s[s.length-1][2]}, 255, 0.5)`:r.color,n.fillText(y,s[s.length-1][0]+4,s[s.length-1][1]+4)};n.font=r.font,a(i.annotations.indexFinger,"index"),a(i.annotations.middleFinger,"middle"),a(i.annotations.ringFinger,"ring"),a(i.annotations.pinky,"pinky"),a(i.annotations.thumb,"thumb"),a(i.annotations.palmBase,"palm")}if(r.drawPolygons){let a=s=>{if(!!s)for(let y=0;y0?y-1:0][0],s[y>0?y-1:0][1]),n.lineTo(s[y][0],s[y][1]),n.stroke()};n.lineWidth=r.lineWidth,a(i.annotations.indexFinger),a(i.annotations.middleFinger),a(i.annotations.ringFinger),a(i.annotations.pinky),a(i.annotations.thumb)}}}}async function xe(A,e,t){let r=q(x0,t);if(!e||!A||!(A instanceof HTMLCanvasElement))return;let n=A.getContext("2d");if(!!n){n.lineJoin="round",n.font=r.font;for(let i of e)if(r.drawBoxes){if(n.strokeStyle=r.color,n.fillStyle=r.color,S0(n,i.box[0],i.box[1],i.box[2],i.box[3],r),r.drawLabels){let a=`${i.label} ${Math.round(100*i.score)}%`;r.shadowColor&&r.shadowColor!==""&&(n.fillStyle=r.shadowColor,n.fillText(a,i.box[0]+3,1+i.box[1]+r.lineHeight,i.box[2])),n.fillStyle=r.labelColor,n.fillText(a,i.box[0]+2,0+i.box[1]+r.lineHeight,i.box[2])}n.stroke()}}}async function o2(A,e,t){let r=q(x0,t);if(!e||!A||!(A instanceof HTMLCanvasElement))return;let n=A.getContext("2d");if(!!n){n.lineJoin="round",n.font=r.font;for(let i=0;iz.box[0]&&P.box[0]z.box[1]&&P.box[1]+P.box[3]m.body.box[0]&&z.box[0]+z.box[2]m.body.box[1]&&z.box[1]+z.box[3]m.body.box[0]&&z.box[1]+z.box[3]>m.body.box[1]&&z.box[1]+z.box[3]{z&&z.length===4&&(S.push(z[0],z[0]+z[2]),I.push(z[1],z[1]+z[3]))};O((g=m.face)==null?void 0:g.box),O((j=m.body)==null?void 0:j.box),O((k=(E=m.hands)==null?void 0:E.left)==null?void 0:k.box),O((u=(T=m.hands)==null?void 0:T.right)==null?void 0:u.box);let W=Math.min(...S),H=Math.min(...I);m.box=[W,H,Math.max(...S)-W,Math.max(...I)-H],n&&n.length===4&&(m.boxRaw=[m.box[0]/n[2],m.box[1]/n[1],m.box[2]/n[2],m.box[3]/n[1]]),a.push(m)}return a}var w={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function le(A){var r,n,i,a,s,y,x,l,d,f,h,v,c,b,g,j,E,k,T,u,P;let e=Date.now()-A.timestamp,t=e<1e3?8-Math.log(e):1;if(w.canvas=A.canvas,!w.body||A.body.length!==w.body.length)w.body=JSON.parse(JSON.stringify(A.body));else for(let m=0;m((t-1)*w.body[m].box[H]+W)/t),I=A.body[m].boxRaw.map((W,H)=>((t-1)*w.body[m].boxRaw[H]+W)/t),O=A.body[m].keypoints.map((W,H)=>({score:W.score,part:W.part,position:[w.body[m].keypoints[H]?((t-1)*w.body[m].keypoints[H].position[0]+W.position[0])/t:W.position[0],w.body[m].keypoints[H]?((t-1)*w.body[m].keypoints[H].position[1]+W.position[1])/t:W.position[1]],positionRaw:[w.body[m].keypoints[H]?((t-1)*w.body[m].keypoints[H].positionRaw[0]+W.positionRaw[0])/t:W.position[0],w.body[m].keypoints[H]?((t-1)*w.body[m].keypoints[H].positionRaw[1]+W.positionRaw[1])/t:W.position[1]]}));w.body[m]={...A.body[m],box:S,boxRaw:I,keypoints:O}}if(!w.hand||A.hand.length!==w.hand.length)w.hand=JSON.parse(JSON.stringify(A.hand));else for(let m=0;m((t-1)*w.hand[m].box[F]+z)/t),I=A.hand[m].boxRaw.map((z,F)=>((t-1)*w.hand[m].boxRaw[F]+z)/t),O=A.hand[m].keypoints.map((z,F)=>z.map((v0,f0)=>((t-1)*w.hand[m].keypoints[F][f0]+v0)/t)),W=Object.keys(A.hand[m].annotations),H={};for(let z of W)H[z]=A.hand[m].annotations[z].map((F,v0)=>F.map((f0,s5)=>((t-1)*w.hand[m].annotations[z][v0][s5]+f0)/t));w.hand[m]={...A.hand[m],box:S,boxRaw:I,keypoints:O,annotations:H}}if(!w.face||A.face.length!==w.face.length)w.face=JSON.parse(JSON.stringify(A.face));else for(let m=0;m((t-1)*w.face[m].box[H]+W)/t),I=A.face[m].boxRaw.map((W,H)=>((t-1)*w.face[m].boxRaw[H]+W)/t),O={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};O.matrix=(r=A.face[m].rotation)==null?void 0:r.matrix,O.angle={roll:((t-1)*(((i=(n=w.face[m].rotation)==null?void 0:n.angle)==null?void 0:i.roll)||0)+(((s=(a=A.face[m].rotation)==null?void 0:a.angle)==null?void 0:s.roll)||0))/t,yaw:((t-1)*(((x=(y=w.face[m].rotation)==null?void 0:y.angle)==null?void 0:x.yaw)||0)+(((d=(l=A.face[m].rotation)==null?void 0:l.angle)==null?void 0:d.yaw)||0))/t,pitch:((t-1)*(((h=(f=w.face[m].rotation)==null?void 0:f.angle)==null?void 0:h.pitch)||0)+(((c=(v=A.face[m].rotation)==null?void 0:v.angle)==null?void 0:c.pitch)||0))/t},O.gaze={bearing:((t-1)*(((g=(b=w.face[m].rotation)==null?void 0:b.gaze)==null?void 0:g.bearing)||0)+(((E=(j=A.face[m].rotation)==null?void 0:j.gaze)==null?void 0:E.bearing)||0))/t,strength:((t-1)*(((T=(k=w.face[m].rotation)==null?void 0:k.gaze)==null?void 0:T.strength)||0)+(((P=(u=A.face[m].rotation)==null?void 0:u.gaze)==null?void 0:P.strength)||0))/t},w.face[m]={...A.face[m],rotation:O,box:S,boxRaw:I}}if(!w.object||A.object.length!==w.object.length)w.object=JSON.parse(JSON.stringify(A.object));else for(let m=0;m((t-1)*w.object[m].box[W]+O)/t),I=A.object[m].boxRaw.map((O,W)=>((t-1)*w.object[m].boxRaw[W]+O)/t);w.object[m]={...A.object[m],box:S,boxRaw:I}}if(A.persons){let m=A.persons;if(!w.persons||m.length!==w.persons.length)w.persons=JSON.parse(JSON.stringify(m));else for(let S=0;S((t-1)*w.persons[S].box[O]+I)/t)}return A.gesture&&(w.gesture=A.gesture),A.performance&&(w.performance=A.performance),w}var $0=` +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __markAsModule = (target) => __defProp(target, "__esModule", { value: true }); +var __export = (target, all2) => { + __markAsModule(target); + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __reExport = (target, module, desc) => { + if (module && typeof module === "object" || typeof module === "function") { + for (let key of __getOwnPropNames(module)) + if (!__hasOwnProp.call(target, key) && key !== "default") + __defProp(target, key, { get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable }); + } + return target; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// src/helpers.ts +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`); + return path; +} +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "webgl", + modelBasePath: "../models/", + wasmPath: "../node_modules/@tensorflow/tfjs-backend-wasm/dist/", + debug: true, + async: true, + warmup: "full", + cacheSensitivity: 0.75, + skipFrame: false, + filter: { + enabled: true, + width: 0, + height: 0, + flip: false, + return: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: true, + maxDetected: 15, + skipFrames: 15, + minConfidence: 0.2, + iouThreshold: 0.1, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 11, + minConfidence: 0.1 + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 17, + modelPath: "emotion.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: 1, + minConfidence: 0.2, + skipFrames: 1 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 18, + minConfidence: 0.1, + iouThreshold: 0.1, + maxDetected: 2, + landmarks: true, + detector: { + modelPath: "handdetect.json" + }, + skeleton: { + modelPath: "handskeleton.json" + } + }, + object: { + enabled: false, + modelPath: "mb3-centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 19 + }, + segmentation: { + enabled: false, + modelPath: "selfie.json" + } +}; + +// src/sysinfo.ts +function info() { + let platform; + let agent; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw && raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + platform = platformMatch ? platformMatch[0].replace(/\(|\)/g, "") : ""; + agent = navigator.userAgent.replace(raw[0], ""); + if (platform[1]) + agent = agent.replace(raw[1], ""); + agent = agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + platform = `${process.platform} ${process.arch}`; + agent = `NodeJS ${process.version}`; + } + return { platform, agent }; +} + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + data: () => data, + version: () => version +}); +__reExport(tfjs_esm_exports, dist_star); +__reExport(tfjs_esm_exports, dist_star2); +__reExport(tfjs_esm_exports, dist_star3); +__reExport(tfjs_esm_exports, dist_star4); +__reExport(tfjs_esm_exports, dist_star5); +__reExport(tfjs_esm_exports, dist_star6); +import { version as tfjsVersion } from "@tensorflow/tfjs/package.json"; +import { version as tfjsCoreVersion } from "@tensorflow/tfjs-core/package.json"; +import { version as tfjsDataVersion } from "@tensorflow/tfjs-data/package.json"; +import { version as tfjsLayersVersion } from "@tensorflow/tfjs-layers/package.json"; +import { version as tfjsConverterVersion } from "@tensorflow/tfjs-converter/package.json"; +import { version as tfjsBackendCPUVersion } from "@tensorflow/tfjs-backend-cpu/package.json"; +import { version as tfjsBackendWebGLVersion } from "@tensorflow/tfjs-backend-webgl/package.json"; +import { version as tfjsBackendWASMVersion } from "@tensorflow/tfjs-backend-wasm/package.json"; +import * as dist_star from "@tensorflow/tfjs-core/dist/index.js"; +import * as dist_star2 from "@tensorflow/tfjs-layers/dist/index.js"; +import * as dist_star3 from "@tensorflow/tfjs-converter/dist/index.js"; +import * as data from "@tensorflow/tfjs-data/dist/index.js"; +import * as dist_star4 from "@tensorflow/tfjs-backend-cpu/dist/index.js"; +import * as dist_star5 from "@tensorflow/tfjs-backend-webgl/dist/index.js"; +import * as dist_star6 from "@tensorflow/tfjs-backend-wasm/dist/index.js"; +var version = { + tfjs: tfjsVersion, + "tfjs-core": tfjsCoreVersion, + "tfjs-data": tfjsDataVersion, + "tfjs-layers": tfjsLayersVersion, + "tfjs-converter": tfjsConverterVersion, + "tfjs-backend-cpu": tfjsBackendCPUVersion, + "tfjs-backend-webgl": tfjsBackendWebGLVersion, + "tfjs-backend-wasm": tfjsBackendWASMVersion +}; + +// src/tfjs/backend.ts +var config2 = { + name: "humangl", + priority: 99, + canvas: null, + gl: null, + width: 1024, + height: 1024, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +function register() { + if (!tfjs_esm_exports.findBackend(config2.name)) { + try { + config2.canvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(config2.width, config2.height) : document.createElement("canvas"); + } catch (err) { + log("error: cannot create canvas:", err); + return; + } + try { + config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); + } catch (err) { + log("error: cannot get WebGL2 context:", err); + return; + } + try { + tfjs_esm_exports.setWebGLContext(2, config2.gl); + } catch (err) { + log("error: cannot set WebGL2 context:", err); + return; + } + try { + const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl); + tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("error: cannot register WebGL backend:", err); + return; + } + try { + const kernels = tfjs_esm_exports.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tfjs_esm_exports.registerKernel(newKernelConfig); + }); + } catch (err) { + log("error: cannot update WebGL backend registration:", err); + return; + } + try { + tfjs_esm_exports.ENV.set("WEBGL_VERSION", 2); + } catch (err) { + log("error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + log("backend registered:", config2.name); + } +} + +// src/blazeface/box.ts +function scaleBoxCoordinates(box6, factor) { + const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]]; + const endPoint = [box6.endPoint[0] * factor[0], box6.endPoint[1] * factor[1]]; + return { startPoint, endPoint }; +} +function getBoxSize(box6) { + return [ + Math.abs(box6.endPoint[0] - box6.startPoint[0]), + Math.abs(box6.endPoint[1] - box6.startPoint[1]) + ]; +} +function getBoxCenter(box6) { + return [ + box6.startPoint[0] + (box6.endPoint[0] - box6.startPoint[0]) / 2, + box6.startPoint[1] + (box6.endPoint[1] - box6.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box6, image18, cropSize) { + const h = image18.shape[1]; + const w = image18.shape[2]; + const boxes = [[ + box6.startPoint[1] / h, + box6.startPoint[0] / w, + box6.endPoint[1] / h, + box6.endPoint[0] / w + ]]; + return tfjs_esm_exports.image.cropAndResize(image18, boxes, [0], cropSize); +} +function enlargeBox(box6, factor = 1.5) { + const center = getBoxCenter(box6); + const size = getBoxSize(box6); + const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, landmarks: box6.landmarks }; +} +function squarifyBox(box6) { + const centers = getBoxCenter(box6); + const size = getBoxSize(box6); + const maxEdge = Math.max(...size); + const halfSize = maxEdge / 2; + const startPoint = [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)]; + const endPoint = [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)]; + return { startPoint, endPoint, landmarks: box6.landmarks }; +} +function calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint, landmarks }; +} +var createBox = (startEndTensor) => ({ + startPoint: tfjs_esm_exports.slice(startEndTensor, [0, 0], [-1, 2]), + endPoint: tfjs_esm_exports.slice(startEndTensor, [0, 2], [-1, 2]) +}); + +// src/blazeface/util.ts +var IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +function normalizeRadians(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians(radians); +} +function buildTranslationMatrix(x, y) { + return [[1, 0, x], [0, 1, y], [0, 0, 1]]; +} +function dot(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices(mat1, mat2) { + const product = []; + const size = mat1.length; + for (let row = 0; row < size; row++) { + product.push([]); + for (let col = 0; col < size; col++) { + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + } + return product; +} +function buildRotationMatrix(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot(rotationComponent[0], translationComponent), + -dot(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint(homogeneousCoordinate, rotationMatrix) { + return [ + dot(homogeneousCoordinate, rotationMatrix[0]), + dot(homogeneousCoordinate, rotationMatrix[1]) + ]; +} +function generateAnchors(inputSize) { + const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize + stride - 1) / stride); + const gridCols = Math.floor((inputSize + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) { + anchors3.push([anchorX, anchorY]); + } + } + } + } + return anchors3; +} + +// src/blazeface/blazeface.ts +var keypointsCount = 6; +function decodeBounds(boxOutputs, anchors3, inputSize) { + const boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]); + const centers = tfjs_esm_exports.add(boxStarts, anchors3); + const boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]); + const boxSizesNormalized = tfjs_esm_exports.div(boxSizes, inputSize); + const centersNormalized = tfjs_esm_exports.div(centers, inputSize); + const halfBoxSize = tfjs_esm_exports.div(boxSizesNormalized, 2); + const starts = tfjs_esm_exports.sub(centersNormalized, halfBoxSize); + const ends = tfjs_esm_exports.add(centersNormalized, halfBoxSize); + const startNormalized = tfjs_esm_exports.mul(starts, inputSize); + const endNormalized = tfjs_esm_exports.mul(ends, inputSize); + const concatAxis = 1; + return tfjs_esm_exports.concat2d([startNormalized, endNormalized], concatAxis); +} +var BlazeFaceModel = class { + constructor(model10, config3) { + this.model = model10; + this.anchorsData = generateAnchors(model10.inputs[0].shape[1]); + this.anchors = tfjs_esm_exports.tensor2d(this.anchorsData); + this.inputSize = model10.inputs[0].shape[2]; + this.config = config3; + } + async getBoundingBoxes(inputImage, userConfig) { + if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return null; + const [batch, boxes, scores] = tfjs_esm_exports.tidy(() => { + const resizedImage = tfjs_esm_exports.image.resizeBilinear(inputImage, [this.inputSize, this.inputSize]); + const normalizedImage = tfjs_esm_exports.sub(tfjs_esm_exports.div(resizedImage, 127.5), 0.5); + const res = this.model.execute(normalizedImage); + let batchOut; + if (Array.isArray(res)) { + const sorted = res.sort((a, b) => a.size - b.size); + const concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2); + const concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2); + const concat3 = tfjs_esm_exports.concat([concat512, concat384], 1); + batchOut = tfjs_esm_exports.squeeze(concat3, 0); + } else { + batchOut = tfjs_esm_exports.squeeze(res); + } + const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]); + const logits = tfjs_esm_exports.slice(batchOut, [0, 0], [-1, 1]); + const scoresOut = tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(logits)); + return [batchOut, boxesOut, scoresOut]; + }); + this.config = mergeDeep(this.config, userConfig); + const nmsTensor = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence); + const nms = await nmsTensor.array(); + tfjs_esm_exports.dispose(nmsTensor); + const annotatedBoxes = []; + const scoresData = await scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scoresData[nms[i]]; + if (confidence > this.config.face.detector.minConfidence) { + const boundingBox = tfjs_esm_exports.slice(boxes, [nms[i], 0], [1, -1]); + const localBox = createBox(boundingBox); + tfjs_esm_exports.dispose(boundingBox); + const anchor = this.anchorsData[nms[i]]; + const landmarks = tfjs_esm_exports.tidy(() => tfjs_esm_exports.reshape(tfjs_esm_exports.squeeze(tfjs_esm_exports.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1])); + annotatedBoxes.push({ box: localBox, landmarks, anchor, confidence }); + } + } + tfjs_esm_exports.dispose(batch); + tfjs_esm_exports.dispose(boxes); + tfjs_esm_exports.dispose(scores); + return { + boxes: annotatedBoxes, + scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize] + }; + } +}; +async function load(config3) { + const model10 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.detector.modelPath), { fromTFHub: config3.face.detector.modelPath.includes("tfhub.dev") }); + const blazeFace = new BlazeFaceModel(model10, config3); + if (!model10 || !model10.modelUrl) + log("load model failed:", config3.face.detector.modelPath); + else if (config3.debug) + log("load model:", model10.modelUrl); + return blazeFace; +} + +// src/blazeface/coords.ts +var MESH_ANNOTATIONS = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], + lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var MESH_TO_IRIS_INDICES_MAP = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, + { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, + { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, + { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, + { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, + { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] } +]; +var UV468 = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + [0.289712011814117, 0.380764007568359], + [0.499954998493195, 0.312398016452789], + [0.499987006187439, 0.269918978214264], + [0.500023007392883, 0.107050001621246], + [0.500023007392883, 0.666234016418457], + [0.5000159740448, 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+ 360, + 440, + 420, + 437, + 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var 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]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); + +// src/blazeface/facepipeline.ts +var leftOutline = MESH_ANNOTATIONS["leftEyeLower0"]; +var rightOutline = MESH_ANNOTATIONS["rightEyeLower0"]; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, MESH_ANNOTATIONS["midwayBetweenEyes"][0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +function replaceRawCoordinates(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) { + const { key, indices } = MESH_TO_IRIS_INDICES_MAP[i]; + const originalIndices = MESH_ANNOTATIONS[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index][0], + newCoords[index][1], + (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var Pipeline = class { + constructor(boundingBoxDetector, meshDetector, irisModel) { + var _a, _b; + this.storedBoxes = []; + this.boundingBoxDetector = boundingBoxDetector; + this.meshDetector = meshDetector; + this.irisModel = irisModel; + this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0; + this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]); + this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0; + this.irisEnlarge = 2.3; + this.skipped = 0; + this.detectedFaces = 0; + } + transformRawCoords(rawCoords, box6, angle, rotationMatrix) { + const boxSize = getBoxSize({ startPoint: box6.startPoint, endPoint: box6.endPoint }); + const coordsScaled = rawCoords.map((coord) => [ + boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2), + boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2), + coord[2] + ]); + const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX; + const coordsRotated = angle !== 0 ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = angle !== 0 ? invertTransformMatrix(rotationMatrix) : IDENTITY_MATRIX; + const boxCenter = [...getBoxCenter({ startPoint: box6.startPoint, endPoint: box6.endPoint }), 1]; + return coordsRotated.map((coord) => [ + Math.round(coord[0] + dot(boxCenter, inverseRotationMatrix[0])), + Math.round(coord[1] + dot(boxCenter, inverseRotationMatrix[1])), + Math.round(coord[2]) + ]); + } + getLeftToRightEyeDepthDifference(rawCoords) { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; + } + getEyeBox(rawCoords, face5, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) { + const box6 = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge)); + const boxSize = getBoxSize(box6); + let crop = tfjs_esm_exports.image.cropAndResize(face5, [[ + box6.startPoint[1] / this.meshSize, + box6.startPoint[0] / this.meshSize, + box6.endPoint[1] / this.meshSize, + box6.endPoint[0] / this.meshSize + ]], [0], [this.irisSize, this.irisSize]); + if (flip && tfjs_esm_exports.ENV.flags.IS_BROWSER) { + const flipped = tfjs_esm_exports.image.flipLeftRight(crop); + tfjs_esm_exports.dispose(crop); + crop = flipped; + } + return { box: box6, boxSize, crop }; + } + getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / this.irisSize : x / this.irisSize) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / this.irisSize * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; + } + getAdjustedIrisCoords(rawCoords, irisCoords, direction) { + const upperCenterZ = rawCoords[MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); + } + correctFaceRotation(config3, box6, input) { + const [indexOfMouth, indexOfForehead] = box6.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + const angle = computeRotation(box6.landmarks[indexOfMouth], box6.landmarks[indexOfForehead]); + const faceCenter = getBoxCenter({ startPoint: box6.startPoint, endPoint: box6.endPoint }); + const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]]; + const rotatedImage = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); + const rotationMatrix = buildRotationMatrix(-angle, faceCenter); + const cut = config3.face.mesh.enabled ? cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, rotatedImage, [this.meshSize, this.meshSize]) : cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, rotatedImage, [this.boxSize, this.boxSize]); + const face5 = tfjs_esm_exports.div(cut, 255); + tfjs_esm_exports.dispose(cut); + tfjs_esm_exports.dispose(rotatedImage); + return [angle, rotationMatrix, face5]; + } + async augmentIris(rawCoords, face5) { + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face5, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face5, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]); + const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]); + tfjs_esm_exports.dispose(leftEyeCrop); + tfjs_esm_exports.dispose(rightEyeCrop); + const eyePredictions = this.irisModel.predict(combined); + tfjs_esm_exports.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tfjs_esm_exports.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize); + const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", null); + replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; + } + async predict(input, config3) { + let useFreshBox = false; + let detector; + if (this.skipped === 0 || this.skipped > config3.face.detector.skipFrames || !config3.face.mesh.enabled || !config3.skipFrame) { + detector = await this.boundingBoxDetector.getBoundingBoxes(input, config3); + this.skipped = 0; + } + if (config3.skipFrame) + this.skipped++; + if (!config3.skipFrame || detector && detector.boxes && (!config3.face.mesh.enabled || detector.boxes.length !== this.detectedFaces && this.detectedFaces !== config3.face.detector.maxDetected)) { + this.storedBoxes = []; + this.detectedFaces = 0; + for (const possible of detector.boxes) { + const startPoint = await possible.box.startPoint.data(); + const endPoint = await possible.box.endPoint.data(); + const landmarks = await possible.landmarks.array(); + this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence }); + } + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + if (useFreshBox) { + if (!detector || !detector.boxes || detector.boxes.length === 0) { + this.storedBoxes = []; + this.detectedFaces = 0; + return null; + } + for (let i = 0; i < this.storedBoxes.length; i++) { + const scaledBox = scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor); + const enlargedBox = enlargeBox(scaledBox); + const squarifiedBox = squarifyBox(enlargedBox); + const landmarks = this.storedBoxes[i].landmarks; + const confidence = this.storedBoxes[i].confidence; + this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks }; + } + } + if (detector && detector.boxes) { + detector.boxes.forEach((prediction) => { + tfjs_esm_exports.dispose(prediction.box.startPoint); + tfjs_esm_exports.dispose(prediction.box.endPoint); + tfjs_esm_exports.dispose(prediction.landmarks); + }); + } + const results = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + let box6 = this.storedBoxes[i]; + let face5; + let angle = 0; + let rotationMatrix; + if (config3.face.detector.rotation && config3.face.mesh.enabled && tfjs_esm_exports.ENV.flags.IS_BROWSER) { + [angle, rotationMatrix, face5] = this.correctFaceRotation(config3, box6, input); + } else { + rotationMatrix = IDENTITY_MATRIX; + const clonedImage = input.clone(); + const cut = config3.face.mesh.enabled ? cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, clonedImage, [this.meshSize, this.meshSize]) : cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, clonedImage, [this.boxSize, this.boxSize]); + face5 = tfjs_esm_exports.div(cut, 255); + tfjs_esm_exports.dispose(cut); + tfjs_esm_exports.dispose(clonedImage); + } + if (!config3.face.mesh.enabled) { + results.push({ + mesh: [], + box: box6, + faceConfidence: null, + boxConfidence: box6.confidence, + confidence: box6.confidence, + image: face5 + }); + } else { + const [contours, confidence, contourCoords] = this.meshDetector.execute(face5); + tfjs_esm_exports.dispose(contours); + const faceConfidence = (await confidence.data())[0]; + tfjs_esm_exports.dispose(confidence); + const coordsReshaped = tfjs_esm_exports.reshape(contourCoords, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + tfjs_esm_exports.dispose(contourCoords); + tfjs_esm_exports.dispose(coordsReshaped); + if (faceConfidence < config3.face.detector.minConfidence) { + this.storedBoxes[i].confidence = faceConfidence; + tfjs_esm_exports.dispose(face5); + } else { + if (config3.face.iris.enabled) + rawCoords = await this.augmentIris(rawCoords, face5); + const mesh = this.transformRawCoords(rawCoords, box6, angle, rotationMatrix); + const storeConfidence = box6.confidence; + box6 = enlargeBox(calculateLandmarksBoundingBox(mesh), 1.5); + box6.confidence = storeConfidence; + if (config3.face.detector.rotation && config3.face.mesh.enabled && config3.face.description.enabled && tfjs_esm_exports.ENV.flags.IS_BROWSER) { + [angle, rotationMatrix, face5] = this.correctFaceRotation(config3, box6, input); + } + results.push({ + mesh, + box: box6, + faceConfidence, + boxConfidence: box6.confidence, + confidence: faceConfidence, + image: face5 + }); + this.storedBoxes[i] = { ...squarifyBox(box6), confidence: box6.confidence, faceConfidence }; + } + } + } + if (config3.face.mesh.enabled) + this.storedBoxes = this.storedBoxes.filter((a) => a.confidence > config3.face.detector.minConfidence); + this.detectedFaces = results.length; + return results; + } +}; + +// src/blazeface/facemesh.ts +var faceModels = [null, null, null]; +var facePipeline; +async function predict(input, config3) { + const predictions = await facePipeline.predict(input, config3); + const results = []; + let id = 0; + for (const prediction of predictions || []) { + if (!prediction || prediction.isDisposedInternal) + continue; + const meshRaw = prediction.mesh.map((pt) => [ + pt[0] / (input.shape[2] || 0), + pt[1] / (input.shape[1] || 0), + pt[2] / facePipeline.meshSize + ]); + const annotations3 = {}; + if (prediction.mesh && prediction.mesh.length > 0) { + for (const key of Object.keys(MESH_ANNOTATIONS)) + annotations3[key] = MESH_ANNOTATIONS[key].map((index) => prediction.mesh[index]); + } + const clampedBox = prediction.box ? [ + Math.trunc(Math.max(0, prediction.box.startPoint[0])), + Math.trunc(Math.max(0, prediction.box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])) + ] : [0, 0, 0, 0]; + const boxRaw3 = prediction.box ? [ + prediction.box.startPoint[0] / (input.shape[2] || 0), + prediction.box.startPoint[1] / (input.shape[1] || 0), + (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0), + (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0) + ] : [0, 0, 0, 0]; + results.push({ + id: id++, + score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100, + boxScore: Math.round(100 * prediction.boxConfidence) / 100, + faceScore: Math.round(100 * prediction.faceConfidence) / 100, + box: clampedBox, + boxRaw: boxRaw3, + mesh: prediction.mesh, + meshRaw, + annotations: annotations3, + tensor: prediction.image + }); + if (prediction.coords) + tfjs_esm_exports.dispose(prediction.coords); + } + return results; +} +async function load2(config3) { + if (!faceModels[0] && config3.face.enabled || !faceModels[1] && config3.face.mesh.enabled || !faceModels[2] && config3.face.iris.enabled) { + faceModels = await Promise.all([ + !faceModels[0] && config3.face.enabled ? load(config3) : null, + !faceModels[1] && config3.face.mesh.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.mesh.modelPath), { fromTFHub: config3.face.mesh.modelPath.includes("tfhub.dev") }) : null, + !faceModels[2] && config3.face.iris.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.iris.modelPath), { fromTFHub: config3.face.iris.modelPath.includes("tfhub.dev") }) : null + ]); + if (config3.face.mesh.enabled) { + if (!faceModels[1] || !faceModels[1]["modelUrl"]) + log("load model failed:", config3.face.mesh.modelPath); + else if (config3.debug) + log("load model:", faceModels[1]["modelUrl"]); + } + if (config3.face.iris.enabled) { + if (!faceModels[2] || !faceModels[2]["modelUrl"]) + log("load model failed:", config3.face.iris.modelPath); + else if (config3.debug) + log("load model:", faceModels[2]["modelUrl"]); + } + } else if (config3.debug) { + if (faceModels[0]) + log("cached model:", faceModels[0].model["modelUrl"]); + if (faceModels[1]) + log("cached model:", faceModels[1]["modelUrl"]); + if (faceModels[2]) + log("cached model:", faceModels[2]["modelUrl"]); + } + facePipeline = new Pipeline(faceModels[0], faceModels[1], faceModels[2]); + return faceModels; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/faceres/faceres.ts +var model; +var last = []; +var lastCount = 0; +var skipped = Number.MAX_SAFE_INTEGER; +async function load3(config3) { + const modelUrl = join(config3.modelBasePath, config3.face.description.modelPath); + if (!model) { + model = await tfjs_esm_exports.loadGraphModel(modelUrl); + if (!model) + log("load model failed:", config3.face.description.modelPath); + else if (config3.debug) + log("load model:", modelUrl); + } else if (config3.debug) + log("cached model:", modelUrl); + return model; +} +function similarity(embedding1, embedding2, order = 2) { + if (!embedding1 || !embedding2) + return 0; + if ((embedding1 == null ? void 0 : embedding1.length) === 0 || (embedding2 == null ? void 0 : embedding2.length) === 0) + return 0; + if ((embedding1 == null ? void 0 : embedding1.length) !== (embedding2 == null ? void 0 : embedding2.length)) + return 0; + const distance = 5 * embedding1.map((_val, i) => Math.abs(embedding1[i] - embedding2[i]) ** order).reduce((sum, now2) => sum + now2, 0) ** (1 / order); + const res = Math.max(0, 100 - distance) / 100; + return res; +} +function match(embedding, db, threshold = 0) { + let best = { similarity: 0, name: "", source: "", embedding: [] }; + if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) + return best; + for (const f of db) { + if (f.embedding && f.name) { + const perc = similarity(embedding, f.embedding); + if (perc > threshold && perc > best.similarity) + best = { ...f, similarity: perc }; + } + } + return best; +} +function enhance(input) { + const image18 = tfjs_esm_exports.tidy(() => { + const tensor2 = input.image || input.tensor || input; + if (!(tensor2 instanceof tfjs_esm_exports.Tensor)) + return null; + const box6 = [[0.05, 0.15, 0.85, 0.85]]; + if (!model.inputs[0].shape) + return null; + const crop = tensor2.shape.length === 3 ? tfjs_esm_exports.image.cropAndResize(tfjs_esm_exports.expandDims(tensor2, 0), box6, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) : tfjs_esm_exports.image.cropAndResize(tensor2, box6, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]); + const norm = tfjs_esm_exports.mul(crop, 255); + return norm; + }); + return image18; +} +async function predict2(image18, config3, idx, count2) { + var _a, _b; + if (!model) + return null; + if (skipped < config3.face.description.skipFrames && config3.skipFrame && lastCount === count2 && ((_a = last[idx]) == null ? void 0 : _a.age) && ((_b = last[idx]) == null ? void 0 : _b.age) > 0) { + skipped++; + return last[idx]; + } + skipped = 0; + return new Promise(async (resolve) => { + const enhanced = enhance(image18); + let resT; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if (config3.face.description.enabled) + resT = await model.predict(enhanced); + tfjs_esm_exports.dispose(enhanced); + if (resT) { + const gender = await resT.find((t) => t.shape[1] === 1).data(); + const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100; + if (confidence > config3.face.description.minConfidence) { + obj.gender = gender[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tfjs_esm_exports.argMax(resT.find((t) => t.shape[1] === 100), 1); + const age = (await argmax.data())[0]; + const all2 = await resT.find((t) => t.shape[1] === 100).data(); + obj.age = Math.round(all2[age - 1] > all2[age + 1] ? 10 * age - 100 * all2[age - 1] : 10 * age + 100 * all2[age + 1]) / 10; + const desc = resT.find((t) => t.shape[1] === 1024); + const descriptor = await desc.data(); + obj.descriptor = [...descriptor]; + resT.forEach((t) => tfjs_esm_exports.dispose(t)); + } + last[idx] = obj; + lastCount = count2; + resolve(obj); + }); +} + +// src/emotion/emotion.ts +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model2; +var last2 = []; +var lastCount2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load4(config3) { + if (!model2) { + model2 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.emotion.modelPath)); + if (!model2 || !model2.modelUrl) + log("load model failed:", config3.face.emotion.modelPath); + else if (config3.debug) + log("load model:", model2.modelUrl); + } else if (config3.debug) + log("cached model:", model2.modelUrl); + return model2; +} +async function predict3(image18, config3, idx, count2) { + if (!model2) + return null; + if (skipped2 < config3.face.emotion.skipFrames && config3.skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) { + skipped2++; + return last2[idx]; + } + skipped2 = 0; + return new Promise(async (resolve) => { + const resize = tfjs_esm_exports.image.resizeBilinear(image18, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false); + const [red, green, blue] = tfjs_esm_exports.split(resize, 3, 3); + tfjs_esm_exports.dispose(resize); + const redNorm = tfjs_esm_exports.mul(red, rgb[0]); + const greenNorm = tfjs_esm_exports.mul(green, rgb[1]); + const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]); + tfjs_esm_exports.dispose(red); + tfjs_esm_exports.dispose(green); + tfjs_esm_exports.dispose(blue); + const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]); + tfjs_esm_exports.dispose(redNorm); + tfjs_esm_exports.dispose(greenNorm); + tfjs_esm_exports.dispose(blueNorm); + const normalize = tfjs_esm_exports.tidy(() => tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, 0.5), 2)); + tfjs_esm_exports.dispose(grayscale); + const obj = []; + if (config3.face.emotion.enabled) { + const emotionT = await model2.predict(normalize); + const data2 = await emotionT.data(); + tfjs_esm_exports.dispose(emotionT); + for (let i = 0; i < data2.length; i++) { + if (data2[i] > config3.face.emotion.minConfidence) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data2[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + } + tfjs_esm_exports.dispose(normalize); + last2[idx] = obj; + lastCount2 = count2; + resolve(obj); + }); +} + +// src/posenet/keypoints.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; + +// src/posenet/utils.ts +function getBoundingBox(keypoints3) { + const coord = keypoints3.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses2, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score: score3, part, position }) => ({ + score: score3, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })) + }); + const scaledPoses = poses2.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max2 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max2; + } + 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(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min, max2) { + if (a < min) + return min; + if (a > max2) + return max2; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/posenet/poses.ts +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors({ x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, { x: offsetPoint.x, y: offsetPoint.y }); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score3 = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score: score3 }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints3 = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints3[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints3[sourceId] && !keypoints3[targetId]) { + keypoints3[targetId] = traverse(edge, keypoints3[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints3[sourceId] && !keypoints3[targetId]) { + keypoints3[targetId] = traverse(edge, keypoints3[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints3; +} +function scoreIsMaximumInLocalWindow(keypointId, score3, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score3) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score: score3 }) => score3); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score3 = scores.get(heatmapY, heatmapX, keypointId); + if (score3 < minConfidence) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score3, heatmapY, heatmapX, scores)) + queue.enqueue({ score: score3, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses2, { x, y }, keypointId) { + return poses2.some(({ keypoints: keypoints3 }) => { + var _a; + const correspondingKeypoint = (_a = keypoints3[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints3) { + const notOverlappedKeypointScores = keypoints3.reduce((result, { position, score: score3 }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score3; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints3.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) { + const poses2 = []; + const queue = buildPartWithScoreQueue(minConfidence, scores); + while (poses2.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses2, rootImageCoords, root.part.id)) + continue; + let keypoints3 = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints3 = keypoints3.filter((a) => a.score > minConfidence); + const score3 = getInstanceScore(poses2, keypoints3); + const box6 = getBoundingBox(keypoints3); + if (score3 > minConfidence) + poses2.push({ keypoints: keypoints3, box: box6, score: Math.round(100 * score3) / 100 }); + } + return poses2; +} + +// src/posenet/posenet.ts +var model3; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +async function predict4(input, config3) { + const res = tfjs_esm_exports.tidy(() => { + if (!model3.inputs[0].shape) + return []; + const resized = tfjs_esm_exports.image.resizeBilinear(input, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]]); + const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1); + const results = model3.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0])); + results3d[1] = results3d[1].sigmoid(); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer())); + for (const t of res) + tfjs_esm_exports.dispose(t); + const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model3.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model3.inputs[0].shape[2], model3.inputs[0].shape[1]]); + return scaled; +} +async function load5(config3) { + if (!model3) { + model3 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath)); + if (!model3 || !model3["modelUrl"]) + log("load model failed:", config3.body.modelPath); + else if (config3.debug) + log("load model:", model3["modelUrl"]); + } else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} + +// src/handpose/box.ts +function getBoxSize2(box6) { + return [ + Math.abs(box6.endPoint[0] - box6.startPoint[0]), + Math.abs(box6.endPoint[1] - box6.startPoint[1]) + ]; +} +function getBoxCenter2(box6) { + return [ + box6.startPoint[0] + (box6.endPoint[0] - box6.startPoint[0]) / 2, + box6.startPoint[1] + (box6.endPoint[1] - box6.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize2(box6, image18, cropSize) { + const h = image18.shape[1]; + const w = image18.shape[2]; + const boxes = [[ + box6.startPoint[1] / h, + box6.startPoint[0] / w, + box6.endPoint[1] / h, + box6.endPoint[0] / w + ]]; + return tfjs_esm_exports.image.cropAndResize(image18, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box6, factor) { + const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]]; + const endPoint = [box6.endPoint[0] * factor[0], box6.endPoint[1] * factor[1]]; + const palmLandmarks = box6.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box6.confidence }; +} +function enlargeBox2(box6, factor = 1.5) { + const center = getBoxCenter2(box6); + const size = getBoxSize2(box6); + const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box6.palmLandmarks }; +} +function squarifyBox2(box6) { + const centers = getBoxCenter2(box6); + const size = getBoxSize2(box6); + const maxEdge = Math.max(...size); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box6.palmLandmarks }; +} + +// src/handpose/anchors.ts +var anchors = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 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0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/handpose/handdetector.ts +var HandDetector = class { + constructor(model10) { + var _a; + this.model = model10; + this.anchors = anchors.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors); + this.inputSize = (_a = this.model) == null ? void 0 : _a.inputs[0].shape[2]; + this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + return tfjs_esm_exports.tidy(() => { + const boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]); + const boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]); + const boxCenterPoints = tfjs_esm_exports.add(tfjs_esm_exports.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor); + const halfBoxSizes = tfjs_esm_exports.div(boxSizes, this.doubleInputSizeTensor); + const startPoints = tfjs_esm_exports.mul(tfjs_esm_exports.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor); + const endPoints = tfjs_esm_exports.mul(tfjs_esm_exports.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor); + return tfjs_esm_exports.concat2d([startPoints, endPoints], 1); + }); + } + normalizeLandmarks(rawPalmLandmarks, index) { + return tfjs_esm_exports.tidy(() => { + const landmarks = tfjs_esm_exports.add(tfjs_esm_exports.div(tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]), this.inputSizeTensor), this.anchors[index]); + return tfjs_esm_exports.mul(landmarks, this.inputSizeTensor); + }); + } + async getBoxes(input, config3) { + const batched = this.model.predict(input); + const predictions = tfjs_esm_exports.squeeze(batched); + tfjs_esm_exports.dispose(batched); + const scoresT = tfjs_esm_exports.tidy(() => tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(tfjs_esm_exports.slice(predictions, [0, 0], [-1, 1])))); + const scores = await scoresT.data(); + const rawBoxes = tfjs_esm_exports.slice(predictions, [0, 1], [-1, 4]); + const boxes = this.normalizeBoxes(rawBoxes); + tfjs_esm_exports.dispose(rawBoxes); + const filteredT = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence); + const filtered = await filteredT.array(); + tfjs_esm_exports.dispose(scoresT); + tfjs_esm_exports.dispose(filteredT); + const hands = []; + for (const index of filtered) { + if (scores[index] >= config3.hand.minConfidence) { + const matchingBox = tfjs_esm_exports.slice(boxes, [index, 0], [1, -1]); + const rawPalmLandmarks = tfjs_esm_exports.slice(predictions, [index, 5], [1, 14]); + const palmLandmarks = tfjs_esm_exports.tidy(() => tfjs_esm_exports.reshape(this.normalizeLandmarks(rawPalmLandmarks, index), [-1, 2])); + tfjs_esm_exports.dispose(rawPalmLandmarks); + hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] }); + } + } + tfjs_esm_exports.dispose(predictions); + tfjs_esm_exports.dispose(boxes); + return hands; + } + async estimateHandBounds(input, config3) { + const inputHeight = input.shape[1]; + const inputWidth = input.shape[2]; + const image18 = tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]), 127.5), 1)); + const predictions = await this.getBoxes(image18, config3); + tfjs_esm_exports.dispose(image18); + const hands = []; + if (!predictions || predictions.length === 0) + return hands; + for (const prediction of predictions) { + const boxes = await prediction.box.data(); + const startPoint = boxes.slice(0, 2); + const endPoint = boxes.slice(2, 4); + const palmLandmarks = await prediction.palmLandmarks.array(); + tfjs_esm_exports.dispose(prediction.box); + tfjs_esm_exports.dispose(prediction.palmLandmarks); + hands.push(scaleBoxCoordinates2({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize])); + } + return hands; + } +}; + +// src/handpose/util.ts +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size = mat1.length; + for (let row = 0; row < size; row++) { + product.push([]); + for (let col = 0; col < size; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/handpose/handpipeline.ts +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + var _a; + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = (_a = this.handPoseModel) == null ? void 0 : _a.inputs[0].shape[2]; + this.storedBoxes = []; + this.skipped = 0; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box22, angle, rotationMatrix) { + const boxSize = getBoxSize2(box22); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box22), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image18, config3) { + let useFreshBox = false; + let boxes; + if (this.skipped === 0 || this.skipped > config3.hand.skipFrames || !config3.hand.landmarks || !config3.skipFrame) { + boxes = await this.handDetector.estimateHandBounds(image18, config3); + this.skipped = 0; + } + if (config3.skipFrame) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image18.shape[2], palmCenter[1] / image18.shape[1]]; + const rotatedImage = config3.hand.rotation && tfjs_esm_exports.ENV.flags.IS_BROWSER ? tfjs_esm_exports.image.rotateWithOffset(image18, angle, 0, palmCenterNormalized) : image18.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tfjs_esm_exports.div(croppedInput, 255); + tfjs_esm_exports.dispose(croppedInput); + tfjs_esm_exports.dispose(rotatedImage); + const [confidenceT, keypoints3] = await this.handPoseModel.predict(handImage); + tfjs_esm_exports.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tfjs_esm_exports.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence) { + const keypointsReshaped = tfjs_esm_exports.reshape(keypoints3, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tfjs_esm_exports.dispose(keypoints3); + tfjs_esm_exports.dispose(keypointsReshaped); + const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords3); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords3, + confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tfjs_esm_exports.dispose(keypoints3); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint } + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + return hands; + } +}; + +// src/handpose/handpose.ts +var meshAnnotations = { + thumb: [1, 2, 3, 4], + indexFinger: [5, 6, 7, 8], + middleFinger: [9, 10, 11, 12], + ringFinger: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palmBase: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict5(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations3 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations)) { + annotations3[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]); + } + } + const keypoints3 = predictions[i].landmarks; + let box6 = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw3 = [0, 0, 0, 0]; + if (keypoints3 && keypoints3.length > 0) { + for (const pt of keypoints3) { + if (pt[0] < box6[0]) + box6[0] = pt[0]; + if (pt[1] < box6[1]) + box6[1] = pt[1]; + if (pt[0] > box6[2]) + box6[2] = pt[0]; + if (pt[1] > box6[3]) + box6[3] = pt[1]; + } + box6[2] -= box6[0]; + box6[3] -= box6[1]; + boxRaw3 = [box6[0] / (input.shape[2] || 0), box6[1] / (input.shape[1] || 0), box6[2] / (input.shape[2] || 0), box6[3] / (input.shape[1] || 0)]; + } else { + box6 = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw3 = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 }); + } + return hands; +} +async function load6(config3) { + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null, + config3.hand.landmarks ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null + ]); + if (config3.hand.enabled) { + if (!handDetectorModel || !handDetectorModel["modelUrl"]) + log("load model failed:", config3.hand.detector.modelPath); + else if (config3.debug) + log("load model:", handDetectorModel["modelUrl"]); + if (!handPoseModel || !handPoseModel["modelUrl"]) + log("load model failed:", config3.hand.skeleton.modelPath); + else if (config3.debug) + log("load model:", handPoseModel["modelUrl"]); + } + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = new HandDetector(handDetectorModel); + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/blazepose/annotations.ts +var full = [ + "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" +]; +var upper = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "left:15", + "right:16", + "left:17", + "right:18", + "left:19", + "right:20", + "left:21", + "right:22", + "leftChest", + "rightChest", + "neck", + "forehead", + "left:27", + "right:28", + "left:29", + "right:30" +]; + +// src/blazepose/blazepose.ts +var model4; +async function load7(config3) { + if (!model4) { + model4 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath)); + model4["width"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[2].size); + model4["height"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[1].size); + if (!model4 || !model4["modelUrl"]) + log("load model failed:", config3.body.modelPath); + else if (config3.debug) + log("load model:", model4["modelUrl"]); + } else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function predict6(image18, config3) { + if (!model4) + return []; + if (!config3.body.enabled) + return []; + const imgSize = { width: image18.shape[2] || 0, height: image18.shape[1] || 0 }; + const resize = tfjs_esm_exports.image.resizeBilinear(image18, [model4["width"], model4["height"]], false); + const normalize = tfjs_esm_exports.div(resize, [255]); + tfjs_esm_exports.dispose(resize); + const resT = await model4.predict(normalize); + const findT = resT.find((t) => t.size === 195 || t.size === 155); + const points = await (findT == null ? void 0 : findT.data()) || []; + resT.forEach((t) => tfjs_esm_exports.dispose(t)); + tfjs_esm_exports.dispose(normalize); + const keypoints3 = []; + const labels2 = (points == null ? void 0 : points.length) === 195 ? full : upper; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + keypoints3.push({ + id: i, + part: labels2[i], + position: [ + Math.trunc(imgSize.width * points[depth * i + 0] / 255), + Math.trunc(imgSize.height * points[depth * i + 1] / 255), + Math.trunc(points[depth * i + 2]) + 0 + ], + positionRaw: [ + points[depth * i + 0] / 255, + points[depth * i + 1] / 255, + points[depth * i + 2] + 0 + ], + score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, + presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100 + }); + } + const x = keypoints3.map((a) => a.position[0]); + const y = keypoints3.map((a) => a.position[1]); + const box6 = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...x) + ]; + const boxRaw3 = [0, 0, 0, 0]; + const score3 = keypoints3.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + return [{ id: 0, score: score3, box: box6, boxRaw: boxRaw3, keypoints: keypoints3 }]; +} + +// src/efficientpose/efficientpose.ts +var model5; +var keypoints = []; +var box4 = [0, 0, 0, 0]; +var boxRaw = [0, 0, 0, 0]; +var score = 0; +var skipped3 = Number.MAX_SAFE_INTEGER; +var bodyParts = ["head", "neck", "rightShoulder", "rightElbow", "rightWrist", "chest", "leftShoulder", "leftElbow", "leftWrist", "pelvis", "rightHip", "rightKnee", "rightAnkle", "leftHip", "leftKnee", "leftAnkle"]; +async function load8(config3) { + if (!model5) { + model5 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath)); + if (!model5 || !model5["modelUrl"]) + log("load model failed:", config3.body.modelPath); + else if (config3.debug) + log("load model:", model5["modelUrl"]); + } else if (config3.debug) + log("cached model:", model5["modelUrl"]); + return model5; +} +function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + return tfjs_esm_exports.tidy(() => { + const mod = (a, b) => tfjs_esm_exports.sub(a, tfjs_esm_exports.mul(tfjs_esm_exports.div(a, tfjs_esm_exports.scalar(b, "int32")), tfjs_esm_exports.scalar(b, "int32"))); + const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]); + const newScore = tfjs_esm_exports.max(reshaped, 0).dataSync()[0]; + if (newScore > minScore) { + const coords3 = tfjs_esm_exports.argMax(reshaped, 0); + const x = mod(coords3, width).dataSync()[0]; + const y = tfjs_esm_exports.div(coords3, tfjs_esm_exports.scalar(width, "int32")).dataSync()[0]; + return [x, y, newScore]; + } + return [0, 0, newScore]; + }); +} +async function predict7(image18, config3) { + if (skipped3 < config3.body.skipFrames && config3.skipFrame && Object.keys(keypoints).length > 0) { + skipped3++; + return [{ id: 0, score, box: box4, boxRaw, keypoints }]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + const tensor2 = tfjs_esm_exports.tidy(() => { + if (!model5.inputs[0].shape) + return null; + const resize = tfjs_esm_exports.image.resizeBilinear(image18, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false); + const enhance2 = tfjs_esm_exports.mul(resize, 2); + const norm = enhance2.sub(1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = await model5.predict(tensor2); + tfjs_esm_exports.dispose(tensor2); + if (resT) { + keypoints.length = 0; + const squeeze7 = resT.squeeze(); + tfjs_esm_exports.dispose(resT); + const stack2 = squeeze7.unstack(2); + tfjs_esm_exports.dispose(squeeze7); + for (let id = 0; id < stack2.length; id++) { + const [x2, y2, partScore] = max2d(stack2[id], config3.body.minConfidence); + if (score > config3.body.minConfidence) { + keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: bodyParts[id], + positionRaw: [ + x2 / model5.inputs[0].shape[2], + y2 / model5.inputs[0].shape[1] + ], + position: [ + Math.round(image18.shape[2] * x2 / model5.inputs[0].shape[2]), + Math.round(image18.shape[1] * y2 / model5.inputs[0].shape[1]) + ] + }); + } + } + stack2.forEach((s) => tfjs_esm_exports.dispose(s)); + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = keypoints.map((a) => a.position[0]); + const y = keypoints.map((a) => a.position[1]); + box4 = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = keypoints.map((a) => a.positionRaw[0]); + const yRaw = keypoints.map((a) => a.positionRaw[1]); + boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + resolve([{ id: 0, score, box: box4, boxRaw, keypoints }]); + }); +} + +// src/movenet/movenet.ts +var model6; +var keypoints2 = []; +var box5 = [0, 0, 0, 0]; +var boxRaw2 = [0, 0, 0, 0]; +var score2 = 0; +var skipped4 = Number.MAX_SAFE_INTEGER; +var bodyParts2 = ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"]; +async function load9(config3) { + if (!model6) { + model6 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath)); + if (!model6 || !model6["modelUrl"]) + log("load model failed:", config3.body.modelPath); + else if (config3.debug) + log("load model:", model6["modelUrl"]); + } else if (config3.debug) + log("cached model:", model6["modelUrl"]); + return model6; +} +async function predict8(image18, config3) { + if (skipped4 < config3.body.skipFrames && config3.skipFrame && Object.keys(keypoints2).length > 0) { + skipped4++; + return [{ id: 0, score: score2, box: box5, boxRaw: boxRaw2, keypoints: keypoints2 }]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + const tensor2 = tfjs_esm_exports.tidy(() => { + if (!model6.inputs[0].shape) + return null; + const resize = tfjs_esm_exports.image.resizeBilinear(image18, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false); + const cast4 = tfjs_esm_exports.cast(resize, "int32"); + return cast4; + }); + let resT; + if (config3.body.enabled) + resT = await model6.predict(tensor2); + tfjs_esm_exports.dispose(tensor2); + if (resT) { + keypoints2.length = 0; + const res = await resT.array(); + tfjs_esm_exports.dispose(resT); + const kpt3 = res[0][0]; + for (let id = 0; id < kpt3.length; id++) { + score2 = kpt3[id][2]; + if (score2 > config3.body.minConfidence) { + keypoints2.push({ + score: Math.round(100 * score2) / 100, + part: bodyParts2[id], + positionRaw: [ + kpt3[id][1], + kpt3[id][0] + ], + position: [ + Math.round((image18.shape[2] || 0) * kpt3[id][1]), + Math.round((image18.shape[1] || 0) * kpt3[id][0]) + ] + }); + } + } + } + score2 = keypoints2.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = keypoints2.map((a) => a.position[0]); + const y = keypoints2.map((a) => a.position[1]); + box5 = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = keypoints2.map((a) => a.positionRaw[0]); + const yRaw = keypoints2.map((a) => a.positionRaw[1]); + boxRaw2 = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + resolve([{ id: 0, score: score2, box: box5, boxRaw: boxRaw2, keypoints: keypoints2 }]); + }); +} + +// src/object/labels.ts +var labels = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/nanodet.ts +var model7; +var last3 = []; +var skipped5 = Number.MAX_SAFE_INTEGER; +var scaleBox = 2.5; +async function load10(config3) { + if (!model7) { + model7 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath)); + const inputs = Object.values(model7.modelSignature["inputs"]); + model7.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null; + if (!model7.inputSize) + throw new Error(`Human: Cannot determine model inputSize: ${config3.object.modelPath}`); + if (!model7 || !model7.modelUrl) + log("load model failed:", config3.object.modelPath); + else if (config3.debug) + log("load model:", model7.modelUrl); + } else if (config3.debug) + log("cached model:", model7.modelUrl); + return model7; +} +async function process2(res, inputSize, outputShape, config3) { + let id = 0; + let results = []; + for (const strideSize of [1, 2, 4]) { + tfjs_esm_exports.tidy(async () => { + var _a, _b; + const baseSize = strideSize * 13; + const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze(); + const featuresT = (_b = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)) == null ? void 0 : _b.squeeze(); + const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); + const boxIdx = await boxesMax.argMax(2).array(); + const scores = await scoresT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < scoresT.shape[1]; j++) { + const score3 = scores[i][j]; + if (score3 > config3.object.minConfidence && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw3 = [x, y, w, h]; + boxRaw3 = boxRaw3.map((a) => Math.max(0, Math.min(a, 1))); + const box6 = [ + boxRaw3[0] * outputShape[0], + boxRaw3[1] * outputShape[1], + boxRaw3[2] * outputShape[0], + boxRaw3[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score3) / 100, + class: j + 1, + label: labels[j].label, + box: box6.map((a) => Math.trunc(a)), + boxRaw: boxRaw3 + }; + results.push(result); + } + } + } + }); + } + res.forEach((t) => tfjs_esm_exports.dispose(t)); + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = await nms.data(); + tfjs_esm_exports.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict9(image18, config3) { + if (skipped5 < config3.object.skipFrames && config3.skipFrame && last3.length > 0) { + skipped5++; + return last3; + } + skipped5 = 0; + return new Promise(async (resolve) => { + const outputSize = [image18.shape[2], image18.shape[1]]; + const resize = tfjs_esm_exports.image.resizeBilinear(image18, [model7.inputSize, model7.inputSize], false); + const norm = tfjs_esm_exports.div(resize, 255); + const transpose = norm.transpose([0, 3, 1, 2]); + tfjs_esm_exports.dispose(norm); + tfjs_esm_exports.dispose(resize); + let objectT; + if (config3.object.enabled) + objectT = await model7.predict(transpose); + tfjs_esm_exports.dispose(transpose); + const obj = await process2(objectT, model7.inputSize, outputSize, config3); + last3 = obj; + resolve(obj); + }); +} + +// src/object/centernet.ts +var model8; +var last4 = []; +var skipped6 = Number.MAX_SAFE_INTEGER; +async function load11(config3) { + if (!model8) { + model8 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath)); + const inputs = Object.values(model8.modelSignature["inputs"]); + model8.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null; + if (!model8.inputSize) + throw new Error(`Human: Cannot determine model inputSize: ${config3.object.modelPath}`); + if (!model8 || !model8.modelUrl) + log("load model failed:", config3.object.modelPath); + else if (config3.debug) + log("load model:", model8.modelUrl); + } else if (config3.debug) + log("cached model:", model8.modelUrl); + return model8; +} +async function process3(res, inputSize, outputShape, config3) { + if (!res) + return []; + const results = []; + const detections = await res.array(); + const squeezeT = tfjs_esm_exports.squeeze(res); + tfjs_esm_exports.dispose(res); + const arr = tfjs_esm_exports.split(squeezeT, 6, 1); + tfjs_esm_exports.dispose(squeezeT); + const stackT = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1); + const boxesT = tfjs_esm_exports.squeeze(stackT); + const scoresT = tfjs_esm_exports.squeeze(arr[4]); + const classesT = tfjs_esm_exports.squeeze(arr[5]); + arr.forEach((t) => tfjs_esm_exports.dispose(t)); + const nmsT = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence); + tfjs_esm_exports.dispose(boxesT); + tfjs_esm_exports.dispose(scoresT); + tfjs_esm_exports.dispose(classesT); + const nms = await nmsT.data(); + tfjs_esm_exports.dispose(nmsT); + let i = 0; + for (const id of nms) { + const score3 = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + const label = labels[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize, + detections[0][id][1] / inputSize + ]; + const boxRaw3 = [ + x, + y, + detections[0][id][2] / inputSize - x, + detections[0][id][3] / inputSize - y + ]; + const box6 = [ + Math.trunc(boxRaw3[0] * outputShape[0]), + Math.trunc(boxRaw3[1] * outputShape[1]), + Math.trunc(boxRaw3[2] * outputShape[0]), + Math.trunc(boxRaw3[3] * outputShape[1]) + ]; + results.push({ id: i++, score: score3, class: classVal, label, box: box6, boxRaw: boxRaw3 }); + } + return results; +} +async function predict10(input, config3) { + if (skipped6 < config3.object.skipFrames && config3.skipFrame && last4.length > 0) { + skipped6++; + return last4; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const outputSize = [input.shape[2], input.shape[1]]; + const resize = tfjs_esm_exports.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]); + const objectT = config3.object.enabled ? model8.execute(resize, ["tower_0/detections"]) : null; + tfjs_esm_exports.dispose(resize); + const obj = await process3(objectT, model8.inputSize, outputSize, config3); + last4 = obj; + resolve(obj); + }); +} + +// src/image/imagefx.js +function GLProgram(gl, vertexSource, fragmentSource) { + const _collect = function(source, prefix, collection) { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match2, name) => { + collection[name] = 0; + return match2; + }); + }; + const _compile = function(source, type) { + const shader = gl.createShader(type); + gl.shaderSource(shader, source); + gl.compileShader(shader); + if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) + throw new Error("Filter: GL compile failed", gl.getShaderInfoLog(shader)); + return shader; + }; + this.uniform = {}; + this.attribute = {}; + const _vsh = _compile(vertexSource, gl.VERTEX_SHADER); + const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER); + this.id = gl.createProgram(); + gl.attachShader(this.id, _vsh); + gl.attachShader(this.id, _fsh); + gl.linkProgram(this.id); + if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) + throw new Error("Filter: GL link failed", gl.getProgramInfoLog(this.id)); + gl.useProgram(this.id); + _collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = gl.getAttribLocation(this.id, a); + _collect(vertexSource, "uniform", this.uniform); + _collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = gl.getUniformLocation(this.id, u); +} +function GLImageFilter(params) { + if (!params) + params = {}; + let _drawCount = 0; + let _sourceTexture = null; + let _lastInChain = false; + let _currentFramebufferIndex = -1; + let _tempFramebuffers = [null, null]; + let _filterChain = []; + let _width = -1; + let _height = -1; + let _vertexBuffer = null; + let _currentProgram = null; + const _filter = {}; + const _canvas = params.canvas || document.createElement("canvas"); + const _shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = _canvas.getContext("webgl"); + if (!gl) + throw new Error("Filter: getContext() failed"); + this.addFilter = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const filter = _filter[name]; + _filterChain.push({ func: filter, args }); + }; + this.reset = function() { + _filterChain = []; + }; + const _resize = function(width, height) { + if (width === _width && height === _height) { + return; + } + _canvas.width = width; + _width = width; + _canvas.height = height; + _height = height; + if (!_vertexBuffer) { + const vertices = new Float32Array([ + -1, + -1, + 0, + 1, + 1, + -1, + 1, + 1, + -1, + 1, + 0, + 0, + -1, + 1, + 0, + 0, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 0 + ]); + _vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, _width, _height); + _tempFramebuffers = [null, null]; + }; + const _createFramebufferTexture = function(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + }; + const _getTempFramebuffer = function(index) { + _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height); + return _tempFramebuffers[index]; + }; + const _draw = function(flags = null) { + var _a, _b; + let source = null; + let target = null; + let flipY = false; + if (_drawCount === 0) { + source = _sourceTexture; + } else { + source = (_a = _getTempFramebuffer(_currentFramebufferIndex)) == null ? void 0 : _a.texture; + } + _drawCount++; + if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = _drawCount % 2 === 0; + } else { + _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2; + target = (_b = _getTempFramebuffer(_currentFramebufferIndex)) == null ? void 0 : _b.fbo; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(_currentProgram.uniform.flipY, flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + }; + this.apply = function(image18) { + _resize(image18.width, image18.height); + _drawCount = 0; + if (!_sourceTexture) + _sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, _sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image18); + if (_filterChain.length === 0) { + _draw(); + return _canvas; + } + for (let i = 0; i < _filterChain.length; i++) { + _lastInChain = i === _filterChain.length - 1; + const f = _filterChain[i]; + f.func.apply(this, f.args || []); + } + return _canvas; + }; + const _compileShader = function(fragmentSource) { + if (_shaderProgramCache[fragmentSource]) { + _currentProgram = _shaderProgramCache[fragmentSource]; + gl.useProgram(_currentProgram.id); + return _currentProgram; + } + const SHADER = {}; + SHADER.VERTEX_IDENTITY = [ + "precision highp float;", + "attribute vec2 pos;", + "attribute vec2 uv;", + "varying vec2 vUv;", + "uniform float flipY;", + "void main(void) {", + "vUv = uv;", + "gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);", + "}" + ].join("\n"); + SHADER.FRAGMENT_IDENTITY = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform sampler2D texture;", + "void main(void) {", + "gl_FragColor = texture2D(texture, vUv);", + "}" + ].join("\n"); + _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource); + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(_currentProgram.attribute.pos); + gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(_currentProgram.attribute.uv); + gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize); + _shaderProgramCache[fragmentSource] = _currentProgram; + return _currentProgram; + }; + _filter.colorMatrix = function(matrix) { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + 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 ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA : _filter.colorMatrix.SHADER.WITH_ALPHA; + const program = _compileShader(shader); + gl.uniform1fv(program.uniform.m, m); + _draw(); + }; + _filter.colorMatrix.SHADER = {}; + _filter.colorMatrix.SHADER.WITH_ALPHA = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform sampler2D texture;", + "uniform float m[20];", + "void main(void) {", + "vec4 c = texture2D(texture, vUv);", + "gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];", + "gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];", + "gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];", + "gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];", + "}" + ].join("\n"); + _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform sampler2D texture;", + "uniform float m[20];", + "void main(void) {", + "vec4 c = texture2D(texture, vUv);", + "gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];", + "gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];", + "gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];", + "gl_FragColor.a = c.a;", + "}" + ].join("\n"); + _filter.brightness = function(brightness) { + const b = (brightness || 0) + 1; + _filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.saturation = function(amount) { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + _filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.desaturate = function() { + _filter.saturation(-1); + }; + _filter.contrast = function(amount) { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + _filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.negative = function() { + _filter.contrast(-2); + }; + _filter.hue = function(rotation) { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + _filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.desaturateLuminance = function() { + _filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.sepia = function() { + _filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.brownie = function() { + _filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.vintagePinhole = function() { + _filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.kodachrome = function() { + _filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.technicolor = function() { + _filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.polaroid = function() { + _filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.shiftToBGR = function() { + _filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }; + _filter.convolution = function(matrix) { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / _width; + const pixelSizeY = 1 / _height; + const program = _compileShader(_filter.convolution.SHADER); + gl.uniform1fv(program.uniform.m, m); + gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY); + _draw(); + }; + _filter.convolution.SHADER = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform sampler2D texture;", + "uniform vec2 px;", + "uniform float m[9];", + "void main(void) {", + "vec4 c11 = texture2D(texture, vUv - px);", + "vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));", + "vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));", + "vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );", + "vec4 c22 = texture2D(texture, vUv);", + "vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );", + "vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );", + "vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );", + "vec4 c33 = texture2D(texture, vUv + px );", + "gl_FragColor = ", + "c11 * m[0] + c12 * m[1] + c22 * m[2] +", + "c21 * m[3] + c22 * m[4] + c23 * m[5] +", + "c31 * m[6] + c32 * m[7] + c33 * m[8];", + "gl_FragColor.a = c22.a;", + "}" + ].join("\n"); + _filter.detectEdges = function() { + _filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }; + _filter.sobelX = function() { + _filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }; + _filter.sobelY = function() { + _filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }; + _filter.sharpen = function(amount) { + const a = amount || 1; + _filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }; + _filter.emboss = function(size) { + const s = size || 1; + _filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }; + _filter.blur = function(size) { + const blurSizeX = size / 7 / _width; + const blurSizeY = size / 7 / _height; + const program = _compileShader(_filter.blur.SHADER); + gl.uniform2f(program.uniform.px, 0, blurSizeY); + _draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform.px, blurSizeX, 0); + _draw(); + }; + _filter.blur.SHADER = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform sampler2D texture;", + "uniform vec2 px;", + "void main(void) {", + "gl_FragColor = vec4(0.0);", + "gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;", + "gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;", + "gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;", + "gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;", + "gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;", + "gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;", + "gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;", + "gl_FragColor += texture2D(texture, vUv )*0.159576912161;", + "gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;", + "gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;", + "gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;", + "gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;", + "gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;", + "gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;", + "gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;", + "}" + ].join("\n"); + _filter.pixelate = function(size) { + const blurSizeX = size / _width; + const blurSizeY = size / _height; + const program = _compileShader(_filter.pixelate.SHADER); + gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY); + _draw(); + }; + _filter.pixelate.SHADER = [ + "precision highp float;", + "varying vec2 vUv;", + "uniform vec2 size;", + "uniform sampler2D texture;", + "vec2 pixelate(vec2 coord, vec2 size) {", + "return floor( coord / size ) * size;", + "}", + "void main(void) {", + "gl_FragColor = vec4(0.0);", + "vec2 coord = pixelate(vUv, size);", + "gl_FragColor += texture2D(texture, coord);", + "}" + ].join("\n"); +} + +// src/image/image.ts +var maxSize = 2048; +var inCanvas; +var outCanvas; +var fx; +function process4(input, config3) { + let tensor2; + if (!input) + throw new Error("Human: Input is missing"); + if (!(input instanceof tfjs_esm_exports.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("Human: Input type is not recognized"); + } + if (input instanceof tfjs_esm_exports.Tensor) { + if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) + tensor2 = tfjs_esm_exports.clone(input); + else + throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`); + } else { + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) + return { tensor: null, canvas: inCanvas }; + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = targetWidth * originalHeight / originalWidth; + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = targetHeight * originalWidth / originalHeight; + } + if (config3.filter.width > 0) + targetWidth = config3.filter.width; + else if (config3.filter.height > 0) + targetWidth = originalWidth * (config3.filter.height / originalHeight); + if (config3.filter.height > 0) + targetHeight = config3.filter.height; + else if (config3.filter.width > 0) + targetHeight = originalHeight * (config3.filter.width / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("Human: Input cannot determine dimension"); + if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight) { + inCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas"); + if ((inCanvas == null ? void 0 : inCanvas.width) !== targetWidth) + inCanvas.width = targetWidth; + if ((inCanvas == null ? void 0 : inCanvas.height) !== targetHeight) + inCanvas.height = targetHeight; + } + const ctx = inCanvas.getContext("2d"); + if (input instanceof ImageData) { + ctx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof ctx.translate !== "undefined") { + ctx.translate(originalWidth, 0); + ctx.scale(-1, 1); + ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } else { + ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height); + } + } + if (config3.filter.enabled) { + if (!fx || !outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height)) { + outCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height) : document.createElement("canvas"); + if ((outCanvas == null ? void 0 : outCanvas.width) !== (inCanvas == null ? void 0 : inCanvas.width)) + outCanvas.width = inCanvas == null ? void 0 : inCanvas.width; + if ((outCanvas == null ? void 0 : outCanvas.height) !== (inCanvas == null ? void 0 : inCanvas.height)) + outCanvas.height = inCanvas == null ? void 0 : inCanvas.height; + fx = tfjs_esm_exports.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null; + } + if (!fx) + return { tensor: null, canvas: inCanvas }; + fx.reset(); + fx.addFilter("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.addFilter("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.addFilter("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.addFilter("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.addFilter("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.addFilter("hue", config3.filter.hue); + if (config3.filter.negative) + fx.addFilter("negative"); + if (config3.filter.sepia) + fx.addFilter("sepia"); + if (config3.filter.vintage) + fx.addFilter("brownie"); + if (config3.filter.sepia) + fx.addFilter("sepia"); + if (config3.filter.kodachrome) + fx.addFilter("kodachrome"); + if (config3.filter.technicolor) + fx.addFilter("technicolor"); + if (config3.filter.polaroid) + fx.addFilter("polaroid"); + if (config3.filter.pixelate !== 0) + fx.addFilter("pixelate", config3.filter.pixelate); + fx.apply(inCanvas); + } else { + outCanvas = inCanvas; + if (fx) + fx = null; + } + if (!tensor2) { + let pixels; + if (outCanvas.data) { + const shape = [outCanvas.height, outCanvas.width, 3]; + pixels = tfjs_esm_exports.tensor3d(outCanvas.data, shape, "int32"); + } else if (outCanvas instanceof ImageData) { + pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(outCanvas) : null; + } else if (config3.backend === "webgl" || config3.backend === "humangl") { + const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas"); + tempCanvas.width = targetWidth; + tempCanvas.height = targetHeight; + const tempCtx = tempCanvas.getContext("2d"); + tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0); + pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(tempCanvas) : null; + } else { + const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas"); + tempCanvas.width = targetWidth; + tempCanvas.height = targetHeight; + const tempCtx = tempCanvas.getContext("2d"); + tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0); + const data2 = tempCtx == null ? void 0 : tempCtx.getImageData(0, 0, targetWidth, targetHeight); + pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(data2) : null; + } + if (pixels) { + const casted = tfjs_esm_exports.cast(pixels, "float32"); + tensor2 = tfjs_esm_exports.expandDims(casted, 0); + tfjs_esm_exports.dispose(pixels); + tfjs_esm_exports.dispose(casted); + } + } + } + const canvas2 = config3.filter.return ? outCanvas : null; + return { tensor: tensor2, canvas: canvas2 }; +} + +// src/segmentation/segmentation.ts +var model9; +var busy = false; +async function load12(config3) { + if (!model9) { + model9 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.segmentation.modelPath)); + if (!model9 || !model9["modelUrl"]) + log("load model failed:", config3.segmentation.modelPath); + else if (config3.debug) + log("load model:", model9["modelUrl"]); + } else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +async function predict11(input) { + var _a, _b; + const width = ((_a = input.tensor) == null ? void 0 : _a.shape[1]) || 0; + const height = ((_b = input.tensor) == null ? void 0 : _b.shape[2]) || 0; + if (!input.tensor) + return null; + if (!model9 || !model9.inputs[0].shape) + return null; + const resizeInput = tfjs_esm_exports.image.resizeBilinear(input.tensor, [model9.inputs[0].shape[1], model9.inputs[0].shape[2]], false); + const norm = tfjs_esm_exports.div(resizeInput, 255); + const res = model9.predict(norm); + tfjs_esm_exports.dispose(resizeInput); + tfjs_esm_exports.dispose(norm); + const squeeze7 = tfjs_esm_exports.squeeze(res, 0); + let resizeOutput; + if (squeeze7.shape[2] === 2) { + const softmax = squeeze7.softmax(); + const [bg, fg] = tfjs_esm_exports.unstack(softmax, 2); + const expand = tfjs_esm_exports.expandDims(fg, 2); + const pad = tfjs_esm_exports.expandDims(expand, 0); + tfjs_esm_exports.dispose(softmax); + tfjs_esm_exports.dispose(bg); + tfjs_esm_exports.dispose(fg); + const crop = tfjs_esm_exports.image.cropAndResize(pad, [[0, 0, 0.5, 0.5]], [0], [width, height]); + resizeOutput = tfjs_esm_exports.squeeze(crop, 0); + tfjs_esm_exports.dispose(crop); + tfjs_esm_exports.dispose(expand); + tfjs_esm_exports.dispose(pad); + } else { + resizeOutput = tfjs_esm_exports.image.resizeBilinear(squeeze7, [width, height]); + } + if (typeof document === "undefined") + return resizeOutput.data(); + const overlay = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas"); + overlay.width = width; + overlay.height = height; + if (tfjs_esm_exports.browser) + await tfjs_esm_exports.browser.toPixels(resizeOutput, overlay); + tfjs_esm_exports.dispose(resizeOutput); + tfjs_esm_exports.dispose(squeeze7); + tfjs_esm_exports.dispose(res); + const alphaCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas"); + alphaCanvas.width = width; + alphaCanvas.height = height; + const ctxAlpha = alphaCanvas.getContext("2d"); + ctxAlpha.filter = "blur(8px"; + await ctxAlpha.drawImage(overlay, 0, 0); + const alpha = ctxAlpha.getImageData(0, 0, width, height).data; + const original = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas"); + original.width = width; + original.height = height; + const ctx = original.getContext("2d"); + if (input.canvas) + await ctx.drawImage(input.canvas, 0, 0); + ctx.globalCompositeOperation = "darken"; + ctx.filter = "blur(8px)"; + await ctx.drawImage(overlay, 0, 0); + ctx.globalCompositeOperation = "source-over"; + ctx.filter = "none"; + input.canvas = original; + return alpha; +} +async function process5(input, background, config3) { + var _a; + if (busy) + return null; + busy = true; + if (!model9) + await load12(config3); + const img = process4(input, config3); + const alpha = await predict11(img); + tfjs_esm_exports.dispose(img.tensor); + if (background && alpha) { + const tmp = process4(background, config3); + const bg = tmp.canvas; + tfjs_esm_exports.dispose(tmp.tensor); + const fg = img.canvas; + const fgData = (_a = fg.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, fg.width, fg.height).data; + const c = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(fg.width, fg.height) : document.createElement("canvas"); + c.width = fg.width; + c.height = fg.height; + const ctx = c.getContext("2d"); + ctx.globalCompositeOperation = "copy"; + ctx.drawImage(bg, 0, 0, c.width, c.height); + const cData = ctx.getImageData(0, 0, c.width, c.height); + for (let i = 0; i < c.width * c.height; i++) { + cData.data[4 * i + 0] = (255 - alpha[4 * i + 0]) / 255 * cData.data[4 * i + 0] + alpha[4 * i + 0] / 255 * fgData[4 * i + 0]; + cData.data[4 * i + 1] = (255 - alpha[4 * i + 1]) / 255 * cData.data[4 * i + 1] + alpha[4 * i + 1] / 255 * fgData[4 * i + 1]; + cData.data[4 * i + 2] = (255 - alpha[4 * i + 2]) / 255 * cData.data[4 * i + 2] + alpha[4 * i + 2] / 255 * fgData[4 * i + 2]; + cData.data[4 * i + 3] = (255 - alpha[4 * i + 3]) / 255 * cData.data[4 * i + 3] + alpha[4 * i + 3] / 255 * fgData[4 * i + 3]; + } + ctx.putImageData(cData, 0, 0); + img.canvas = c; + } + busy = false; + return img.canvas; +} + +// src/models.ts +async function load13(instance) { + if (instance.config.async) { + [ + instance.models.face, + instance.models.emotion, + instance.models.handpose, + instance.models.posenet, + instance.models.blazepose, + instance.models.efficientpose, + instance.models.movenet, + instance.models.nanodet, + instance.models.centernet, + instance.models.faceres, + instance.models.segmentation + ] = await Promise.all([ + instance.models.face || (instance.config.face.enabled ? load2(instance.config) : null), + instance.models.emotion || (instance.config.face.enabled && instance.config.face.emotion.enabled ? load4(instance.config) : null), + instance.models.handpose || (instance.config.hand.enabled ? load6(instance.config) : null), + instance.models.posenet || (instance.config.body.enabled && instance.config.body.modelPath.includes("posenet") ? load5(instance.config) : null), + instance.models.blazepose || (instance.config.body.enabled && instance.config.body.modelPath.includes("blazepose") ? load7(instance.config) : null), + instance.models.efficientpose || (instance.config.body.enabled && instance.config.body.modelPath.includes("efficientpose") ? load8(instance.config) : null), + instance.models.movenet || (instance.config.body.enabled && instance.config.body.modelPath.includes("movenet") ? load9(instance.config) : null), + instance.models.nanodet || (instance.config.object.enabled && instance.config.object.modelPath.includes("nanodet") ? load10(instance.config) : null), + instance.models.centernet || (instance.config.object.enabled && instance.config.object.modelPath.includes("centernet") ? load11(instance.config) : null), + instance.models.faceres || (instance.config.face.enabled && instance.config.face.description.enabled ? load3(instance.config) : null), + instance.models.segmentation || (instance.config.segmentation.enabled ? load12(instance.config) : null) + ]); + } else { + if (instance.config.face.enabled && !instance.models.face) + instance.models.face = await load2(instance.config); + if (instance.config.face.enabled && instance.config.face.emotion.enabled && !instance.models.emotion) + instance.models.emotion = await load4(instance.config); + if (instance.config.hand.enabled && !instance.models.handpose) + instance.models.handpose = await load6(instance.config); + if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath.includes("posenet")) + instance.models.posenet = await load5(instance.config); + if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath.includes("blazepose")) + instance.models.blazepose = await load7(instance.config); + if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath.includes("efficientpose")) + instance.models.efficientpose = await load7(instance.config); + if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath.includes("movenet")) + instance.models.movenet = await load9(instance.config); + if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath.includes("nanodet")) + instance.models.nanodet = await load10(instance.config); + if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath.includes("centernet")) + instance.models.centernet = await load11(instance.config); + if (instance.config.face.enabled && instance.config.face.description.enabled && !instance.models.faceres) + instance.models.faceres = await load3(instance.config); + if (instance.config.segmentation.enabled && !instance.models.segmentation) + instance.models.segmentation = await load12(instance.config); + } +} + +// src/face.ts +var calculateGaze = (face5) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face5.annotations["rightEyeIris"] || !face5.annotations["leftEyeIris"]) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = face5.mesh[33][2] > face5.mesh[263][2]; + const irisCenter = left ? face5.mesh[473] : face5.mesh[468]; + const eyeCenter = left ? [(face5.mesh[133][0] + face5.mesh[33][0]) / 2, (face5.mesh[133][1] + face5.mesh[33][1]) / 2] : [(face5.mesh[263][0] + face5.mesh[362][0]) / 2, (face5.mesh[263][1] + face5.mesh[362][1]) / 2]; + const eyeSize = left ? [face5.mesh[133][0] - face5.mesh[33][0], face5.mesh[23][1] - face5.mesh[27][1]] : [face5.mesh[263][0] - face5.mesh[362][0], face5.mesh[253][1] - face5.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] ** 2 + eyeDiff[1] ** 2); + strength = Math.min(strength, face5.boxRaw[2] / 2, face5.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face5, imageSize) => { + const normalize = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const meshToEulerAngle = (mesh2) => { + const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1); + const angle2 = { + pitch: radians(mesh2[10][1], mesh2[10][2], mesh2[152][1], mesh2[152][2]), + yaw: radians(mesh2[33][0], mesh2[33][2], mesh2[263][0], mesh2[263][2]), + roll: radians(mesh2[33][0], mesh2[33][1], mesh2[263][0], mesh2[263][1]) + }; + return angle2; + }; + const mesh = face5.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size = Math.max(face5.boxRaw[2] * imageSize[0], face5.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [ + pt[0] * imageSize[0] / size, + pt[1] * imageSize[1] / size, + pt[2] + ]); + const y_axis = normalize(subVectors(pts[1], pts[0])); + let x_axis = normalize(subVectors(pts[3], pts[2])); + const z_axis = normalize(crossVectors(x_axis, y_axis)); + x_axis = crossVectors(y_axis, z_axis); + const matrix = [ + x_axis[0], + x_axis[1], + x_axis[2], + y_axis[0], + y_axis[1], + y_axis[2], + z_axis[0], + z_axis[1], + z_axis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face5) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; +var detectFace = async (parent, input) => { + var _a, _b, _c, _d, _e, _f; + let timeStamp; + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let embeddingRes; + let descRes; + const faceRes = []; + parent.state = "run:face"; + timeStamp = now(); + const faces = await predict(input, parent.config); + parent.performance.face = Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + parent.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]); + parent.analyze("Start Emotion:"); + if (parent.config.async) { + emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : {}; + } else { + parent.state = "run:emotion"; + timeStamp = now(); + emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : {}; + parent.performance.emotion = Math.trunc(now() - timeStamp); + } + parent.analyze("End Emotion:"); + parent.analyze("Start Description:"); + if (parent.config.async) { + descRes = parent.config.face.description.enabled ? predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : []; + } else { + parent.state = "run:description"; + timeStamp = now(); + descRes = parent.config.face.description.enabled ? await predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : []; + parent.performance.embedding = Math.trunc(now() - timeStamp); + } + parent.analyze("End Description:"); + if (parent.config.async) { + [ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes]); + } + parent.analyze("Finish Face:"); + if (!parent.config.face.iris.enabled && ((_b = (_a = faces[i]) == null ? void 0 : _a.annotations) == null ? void 0 : _b.leftEyeIris) && ((_d = (_c = faces[i]) == null ? void 0 : _c.annotations) == null ? void 0 : _d.rightEyeIris)) { + delete faces[i].annotations.leftEyeIris; + delete faces[i].annotations.rightEyeIris; + } + const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0; + const tensor2 = parent.config.face.detector.return ? tfjs_esm_exports.squeeze(faces[i].tensor) : null; + tfjs_esm_exports.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + faceRes.push({ + ...faces[i], + id: i, + age: descRes.age, + gender: descRes.gender, + genderScore: descRes.genderScore, + embedding: descRes.descriptor, + emotion: emotionRes, + iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0, + rotation, + tensor: tensor2 + }); + parent.analyze("End Face"); + } + parent.analyze("End FaceMesh:"); + if (parent.config.async) { + if (parent.performance.face) + delete parent.performance.face; + if (parent.performance.age) + delete parent.performance.age; + if (parent.performance.gender) + delete parent.performance.gender; + if (parent.performance.emotion) + delete parent.performance.emotion; + } + return faceRes; +}; + +// src/gesture/gesture.ts +var body = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position.y < nose.position.y && rightWrist.position.y < nose.position.y) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position.y < nose.position.y) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position.y < nose.position.y) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder) + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position.y > rightShoulder.position.y ? "left" : "right"}` }); + } + return gestures; +}; +var face = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 0) { + const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2]; + if (Math.abs(eyeFacing) < 10) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? "left" : "right"}` }); + 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]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + 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]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + 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])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2]; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > 0.06) + gestures.push({ iris: i, gesture: "looking right" }); + if (rightIrisCenterX > 0.06) + gestures.push({ iris: i, gesture: "looking left" }); + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + for (const [finger, pos] of Object.entries(res[i]["annotations"])) { + if (finger !== "palmBase" && Array.isArray(pos)) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + } + return gestures; +}; + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body2, + canvas: () => canvas, + face: () => face2, + gesture: () => gesture, + hand: () => hand2, + object: () => object, + options: () => options, + person: () => person +}); +var options = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + font: 'small-caps 14px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false, + bufferedOutput: true +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +function point(ctx, x, y, z = 0, localOptions) { + ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color; + ctx.beginPath(); + ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions) { + ctx.beginPath(); + if (localOptions.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.lineWidth = localOptions.lineWidth; + ctx.moveTo(x + localOptions.roundRect, y); + ctx.lineTo(x + width - localOptions.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect); + ctx.lineTo(x + width, y + height - localOptions.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height); + ctx.lineTo(x + localOptions.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect); + ctx.lineTo(x, y + localOptions.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points = [], localOptions) { + if (points === void 0 || points.length === 0) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + const z = pt[2] || 0; + ctx.strokeStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color; + ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color; + ctx.lineTo(pt[0], Math.round(pt[1])); + } + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points = [], localOptions) { + if (points === void 0 || points.length === 0) + return; + if (!localOptions.useCurves || points.length <= 2) { + lines(ctx, points, localOptions); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +async function gesture(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.fillStyle = localOptions.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + let where = []; + let what = []; + [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + const label = `${where[0]} ${who}: ${what[1]}`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, 8, 2 + i * localOptions.lineHeight); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, 6, 0 + i * localOptions.lineHeight); + i += 1; + } + } +} +async function face2(inCanvas2, result, drawOptions) { + var _a, _b, _c, _d; + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + for (const f of result) { + ctx.font = localOptions.font; + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + if (localOptions.drawBoxes) + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); + const labels2 = []; + labels2.push(`face: ${Math.trunc(100 * f.score)}%`); + if (f.genderScore) + labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`); + if (f.age) + labels2.push(`age: ${f.age || ""}`); + if (f.iris) + labels2.push(`distance: ${f.iris}`); + if (f.emotion && f.emotion.length > 0) { + const emotion3 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion3.length > 3) + emotion3.length = 3; + labels2.push(emotion3.join(" ")); + } + if (f.rotation && f.rotation.angle && f.rotation.gaze) { + if (f.rotation.angle.roll) + labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`); + if (f.rotation.gaze.bearing) + labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`); + } + if (labels2.length === 0) + labels2.push("face"); + ctx.fillStyle = localOptions.color; + for (let i = labels2.length - 1; i >= 0; i--) { + const x = Math.max(f.box[0], 0); + const y = i * localOptions.lineHeight + f.box[1]; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(labels2[i], x + 5, y + 16); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(labels2[i], x + 4, y + 15); + } + ctx.lineWidth = 1; + if (f.mesh && f.mesh.length > 0) { + if (localOptions.drawPoints) { + for (const pt of f.mesh) + point(ctx, pt[0], pt[1], pt[2], localOptions); + } + if (localOptions.drawPolygons) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [ + TRI468[i * 3 + 0], + TRI468[i * 3 + 1], + TRI468[i * 3 + 2] + ].map((index) => f.mesh[index]); + lines(ctx, points, localOptions); + } + if (f.annotations && f.annotations["leftEyeIris"]) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations["leftEyeIris"][3][0] - f.annotations["leftEyeIris"][1][0]) / 2; + const sizeY = Math.abs(f.annotations["leftEyeIris"][4][1] - f.annotations["leftEyeIris"][2][1]) / 2; + ctx.ellipse(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (f.annotations && f.annotations["rightEyeIris"]) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations["rightEyeIris"][3][0] - f.annotations["rightEyeIris"][1][0]) / 2; + const sizeY = Math.abs(f.annotations["rightEyeIris"][4][1] - f.annotations["rightEyeIris"][2][1]) / 2; + ctx.ellipse(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) { + ctx.strokeStyle = "pink"; + ctx.beginPath(); + const leftGaze = [ + f.annotations["leftEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations["leftEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + ctx.moveTo(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1]); + ctx.lineTo(leftGaze[0], leftGaze[1]); + const rightGaze = [ + f.annotations["rightEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations["rightEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + ctx.moveTo(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1]); + ctx.lineTo(rightGaze[0], rightGaze[1]); + ctx.stroke(); + } + } + } + } +} +async function body2(inCanvas2, result, drawOptions) { + var _a; + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + ctx.lineWidth = localOptions.lineWidth; + ctx.font = localOptions.font; + if (localOptions.drawBoxes && result[i].box && ((_a = result[i].box) == null ? void 0 : _a.length) === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions); + if (localOptions.drawLabels) { + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + } + if (localOptions.drawPoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + 2 * (result[i].keypoints[pt].position[2] || 0)}, ${127.5 - 2 * (result[i].keypoints[pt].position[2] || 0)}, 255, 0.5)` : localOptions.color; + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions); + } + } + if (localOptions.drawLabels) { + ctx.font = localOptions.font; + if (result[i].keypoints) { + for (const pt of result[i].keypoints) { + ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + 2 * pt.position[2]}, ${127.5 - 2 * pt.position[2]}, 255, 0.5)` : localOptions.color; + ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4); + } + } + } + if (localOptions.drawPolygons && result[i].keypoints) { + let part; + const points = []; + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "leftShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + curves(ctx, points, localOptions); + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "rightShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightHip"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftHip"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + if (points.length === 4) + lines(ctx, points, localOptions); + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "leftHip"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftKnee"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftAnkle"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftHeel"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftFoot"); + if (part) + points.push([part.position[0], part.position[1]]); + curves(ctx, points, localOptions); + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "rightHip"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightKnee"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightAnkle"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightHeel"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightFoot"); + if (part) + points.push([part.position[0], part.position[1]]); + curves(ctx, points, localOptions); + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "leftShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftElbow"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftWrist"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "leftPalm"); + if (part) + points.push([part.position[0], part.position[1]]); + curves(ctx, points, localOptions); + points.length = 0; + part = result[i].keypoints.find((a) => a.part === "rightShoulder"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightElbow"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightWrist"); + if (part) + points.push([part.position[0], part.position[1]]); + part = result[i].keypoints.find((a) => a.part === "rightPalm"); + if (part) + points.push([part.position[0], part.position[1]]); + curves(ctx, points, localOptions); + } + } +} +async function hand2(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (const h of result) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions); + if (localOptions.drawLabels) { + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText("hand", h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText("hand", h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.stroke(); + } + if (localOptions.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * pt[2]}, ${127.5 - 2 * pt[2]}, 255, 0.5)` : localOptions.color; + point(ctx, pt[0], pt[1], 0, localOptions); + } + } + } + if (localOptions.drawLabels) { + const addHandLabel = (part, title) => { + ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * part[part.length - 1][2]}, ${127.5 - 2 * part[part.length - 1][2]}, 255, 0.5)` : localOptions.color; + ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4); + }; + ctx.font = localOptions.font; + addHandLabel(h.annotations["indexFinger"], "index"); + addHandLabel(h.annotations["middleFinger"], "middle"); + addHandLabel(h.annotations["ringFinger"], "ring"); + addHandLabel(h.annotations["pinky"], "pinky"); + addHandLabel(h.annotations["thumb"], "thumb"); + addHandLabel(h.annotations["palmBase"], "palm"); + } + if (localOptions.drawPolygons) { + const addHandLine = (part) => { + if (!part) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * part[i][2]}, ${127.5 - 2 * part[i][2]}, 255, 0.5)` : localOptions.color; + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions.lineWidth; + addHandLine(h.annotations["indexFinger"]); + addHandLine(h.annotations["middleFinger"]); + addHandLine(h.annotations["ringFinger"]); + addHandLine(h.annotations["pinky"]); + addHandLine(h.annotations["thumb"]); + } + } +} +async function object(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (const h of result) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions); + if (localOptions.drawLabels) { + const label = `${h.label} ${Math.round(100 * h.score)}%`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.stroke(); + } + } +} +async function person(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + const ctx = inCanvas2.getContext("2d"); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (let i = 0; i < result.length; i++) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions); + if (localOptions.drawLabels) { + const label = `person #${i}`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +async function canvas(inCanvas2, outCanvas2) { + if (!inCanvas2 || !outCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement) || !(outCanvas2 instanceof HTMLCanvasElement)) + return; + const outCtx = inCanvas2.getContext("2d"); + outCtx == null ? void 0 : outCtx.drawImage(inCanvas2, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + const timestamp = now(); + const localOptions = mergeDeep(options, drawOptions); + if (!result || !inCanvas2) + return; + if (!(inCanvas2 instanceof HTMLCanvasElement)) + return; + face2(inCanvas2, result.face, localOptions); + body2(inCanvas2, result.body, localOptions); + hand2(inCanvas2, result.hand, localOptions); + object(inCanvas2, result.object, localOptions); + gesture(inCanvas2, result.gesture, localOptions); + result.performance.draw = Math.trunc(now() - timestamp); +} + +// src/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p; + let id = 0; + const persons2 = []; + for (const face5 of faces) { + const person2 = { id: id++, face: face5, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face5.box[0] > body4.box[0] && face5.box[0] < body4.box[0] + body4.box[2] && face5.box[1] + face5.box[3] > body4.box[1] && face5.box[1] + face5.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture3 of gestures) { + if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id) + (_a = person2.gestures) == null ? void 0 : _a.push(gesture3); + else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id) + (_b = person2.gestures) == null ? void 0 : _b.push(gesture3); + else if (gesture3["body"] !== void 0 && gesture3["body"] === ((_c = person2.body) == null ? void 0 : _c.id)) + (_d = person2.gestures) == null ? void 0 : _d.push(gesture3); + else if (gesture3["hand"] !== void 0 && gesture3["hand"] === ((_f = (_e = person2.hands) == null ? void 0 : _e.left) == null ? void 0 : _f.id)) + (_g = person2.gestures) == null ? void 0 : _g.push(gesture3); + else if (gesture3["hand"] !== void 0 && gesture3["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id)) + (_j = person2.gestures) == null ? void 0 : _j.push(gesture3); + } + const x = []; + const y = []; + const extractXY = (box6) => { + if (box6 && box6.length === 4) { + x.push(box6[0], box6[0] + box6[2]); + y.push(box6[1], box6[1] + box6[3]); + } + }; + extractXY((_k = person2.face) == null ? void 0 : _k.box); + extractXY((_l = person2.body) == null ? void 0 : _l.box); + extractXY((_n = (_m = person2.hands) == null ? void 0 : _m.left) == null ? void 0 : _n.box); + extractXY((_p = (_o = person2.hands) == null ? void 0 : _o.right) == null ? void 0 : _p.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if (shape && shape.length === 4) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons2.push(person2); + } + return persons2; +} + +// src/interpolate.ts +var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 }; +function calc(newResult) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed) : 1; + bufferedResult.canvas = newResult.canvas; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box6 = newResult.body[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + b) / bufferedFactor); + const boxRaw3 = newResult.body[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + b) / bufferedFactor); + const keypoints3 = newResult.body[i].keypoints.map((keypoint, j) => ({ + score: keypoint.score, + part: keypoint.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[0] + keypoint.position[0]) / bufferedFactor : keypoint.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[1] + keypoint.position[1]) / bufferedFactor : keypoint.position[1] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[0] + keypoint.positionRaw[0]) / bufferedFactor : keypoint.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[1] + keypoint.positionRaw[1]) / bufferedFactor : keypoint.position[1] + ] + })); + bufferedResult.body[i] = { ...newResult.body[i], box: box6, boxRaw: boxRaw3, keypoints: keypoints3 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box6 = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw3 = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + const keypoints3 = newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].keypoints[j][k] + coord) / bufferedFactor)); + const keys = Object.keys(newResult.hand[i].annotations); + const annotations3 = {}; + for (const key of keys) { + annotations3[key] = newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)); + } + bufferedResult.hand[i] = { ...newResult.hand[i], box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 }; + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box6 = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw3 = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box: box6, boxRaw: boxRaw3 }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box6, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box6) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + if (newResult.performance) + bufferedResult.performance = newResult.performance; + return bufferedResult; +} + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -162,7 +10729,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,A5=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -730,5 +11298,472 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var de="2.1.3";var T0,k0,I0,d0,c0,P0,e5,N0,t5,o5,n5,r5,a2=class{constructor(e){K(this,T0,void 0);K(this,k0,void 0);K(this,I0,void 0);K(this,d0,void 0);K(this,c0,void 0);K(this,P0,void 0);this.analyze=(...e)=>{if(!C(this,k0))return;let t=this.tf.engine().state.numTensors,r=C(this,T0);_(this,T0,t);let n=t-r;n!==0&&M(...e,n)};K(this,e5,e=>{if(!C(this,I0))return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof o.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(t){return"backend not loaded"}return null});K(this,N0,async(e=!1)=>{var t;if(this.config.backend&&this.config.backend.length>0&&e||this.tf.getBackend()!==this.config.backend){let r=N();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&M("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&M("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&M("wasm path:",this.config.wasmPath),typeof((t=this.tf)==null?void 0:t.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let n=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),i=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&M(`wasm execution: ${n?"SIMD":"no SIMD"} ${i?"multithreaded":"singlethreaded"}`),this.config.debug&&!n&&M("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&dA();try{await this.tf.setBackend(this.config.backend)}catch(n){M("error: cannot set backend:",this.config.backend,n)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!1),this.tf.ENV.set("WEBGL_USE_SHAPES_UNIFORMS",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(M("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let n=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&M(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(N()-r)}});this.next=e=>le(e||this.result);K(this,t5,async e=>{if(this.config.cacheSensitivity===0)return!1;let t=32,r=o.image.resizeBilinear(e,[Math.trunc(e.shape[1]/t),Math.trunc(e.shape[2]/t)]),n=await r.data(),i=0;for(let y=0;y10*this.config.cacheSensitivity?0:a),s});K(this,o5,async()=>{let e=(n,i="application/octet-stream")=>fetch(`data:${i};base64,${n}`).then(a=>a.blob()),t,r;switch(this.config.warmup){case"face":t=await e($0);break;case"full":t=await e(A5);break;default:t=null}if(t){let n=await createImageBitmap(t);r=await this.detect(n,this.config),n.close()}return r});K(this,n5,async()=>new Promise(e=>{let t,r=0;switch(this.config.warmup){case"face":r=256,t="data:image/jpeg;base64,"+$0;break;case"full":case"body":r=1200,t="data:image/jpeg;base64,"+A5;break;default:t=null}let n=new Image;n.onload=async()=>{let i=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");i.width=n.naturalWidth,i.height=n.naturalHeight;let a=i.getContext("2d");a==null||a.drawImage(n,0,0);let s=await this.detect(i,this.config);e(s)},t?n.src=t:e(null)}));K(this,r5,async()=>{let e=n=>Buffer.from(n,"base64"),t;if(this.config.warmup==="face"&&(t=e($0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(t=e(A5)),!t)return null;let r;if(typeof o.node!="undefined"){let n=o.node.decodeJpeg(t),i=n.expandDims(0);this.tf.dispose(n),r=await this.detect(i,this.config),this.tf.dispose(i)}else this.config.debug&&M("Warmup tfjs-node not loaded");return r});this.config=q(yA,e||{}),this.tf=o,this.draw=aA,this.version=de,this.state="idle",_(this,T0,0),_(this,k0,!1),_(this,I0,!1),_(this,d0,!0),_(this,P0,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.image=t=>l0(t,this.config),this.faceTriangulation=zA,this.faceUVMap=EA,this.sysinfo=lA(),_(this,c0,1)}similarity(e,t){return p5(e,t)}segmentation(e,t){return $A(e,t,this.config)}enhance(e){return b5(e)}match(e,t,r=0){return jA(e,t,r)}async load(e){this.state="load";let t=N();e&&(this.config=q(this.config,e)),C(this,d0)&&(this.config.debug&&M(`version: ${this.version}`),this.config.debug&&M(`tfjs version: ${this.tf.version_core}`),this.config.debug&&M("platform:",this.sysinfo.platform),this.config.debug&&M("agent:",this.sysinfo.agent),await C(this,N0).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&M("configuration:",this.config),this.config.debug&&M("tf flags:",this.tf.ENV.flags))),await Ae(this),C(this,d0)&&(this.config.debug&&M("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),_(this,d0,!1));let r=Math.trunc(N()-t);r>(this.performance.load||0)&&(this.performance.load=r)}async detect(e,t){return new Promise(async r=>{this.state="config";let n,i;this.config=q(this.config,t),this.state="check";let a=C(this,e5).call(this,e);a&&(M(a,e),r({error:a}));let s=N();await C(this,N0).call(this),await this.load(),n=N();let y=l0(e,this.config);if(this.performance.image=Math.trunc(N()-n),this.analyze("Get Image:"),this.config.segmentation.enabled&&y&&y.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",n=N(),await oA(y),i=Math.trunc(N()-n),i>0&&(this.performance.segmentation=i),y.canvas&&(o.dispose(y.tensor),y=l0(y.canvas,this.config)),this.analyze("End Segmentation:")),!y||!y.tensor){M("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}n=N(),this.config.skipFrame=await C(this,t5).call(this,y.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(N()-n),this.analyze("Check Changed:");let x,l,d,f;this.config.async?(x=this.config.face.enabled?nA(this,y.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",n=N(),x=this.config.face.enabled?await nA(this,y.tensor):[],i=Math.trunc(N()-n),i>0&&(this.performance.face=i)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?S5(y.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?Z5(y.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?C5(y.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?J5(y.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",n=N(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await S5(y.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await Z5(y.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?await C5(y.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?await J5(y.tensor,this.config):[]),i=Math.trunc(N()-n),i>0&&(this.performance.body=i)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?H5(y.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",n=N(),d=this.config.hand.enabled?await H5(y.tensor,this.config):[],i=Math.trunc(N()-n),i>0&&(this.performance.hand=i)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?f=this.config.object.enabled?Q5(y.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(f=this.config.object.enabled?eA(y.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",n=N(),this.config.object.modelPath.includes("nanodet")?f=this.config.object.enabled?await Q5(y.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(f=this.config.object.enabled?await eA(y.tensor,this.config):[]),i=Math.trunc(N()-n),i>0&&(this.performance.object=i)),this.analyze("End Object:"),this.config.async&&([x,l,d,f]=await Promise.all([x,l,d,f]));let h=[];this.config.gesture.enabled&&(n=N(),h=[...te(x),...ee(l),...ne(d),...oe(x)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(N()-n)),this.performance.total=Math.trunc(N()-s),this.state="idle",this.result={face:x,body:l,hand:d,gesture:h,object:f,performance:this.performance,canvas:y.canvas,timestamp:Date.now(),get persons(){var v;return ye(x,l,d,h,(v=y==null?void 0:y.tensor)==null?void 0:v.shape)}},o.dispose(y.tensor),r(this.result)})}async warmup(e){let t=N();if(e&&(this.config=q(this.config,e)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r;typeof createImageBitmap=="function"?r=await C(this,o5).call(this):typeof Image!="undefined"?r=await C(this,n5).call(this):r=await C(this,r5).call(this);let n=N();return this.config.debug&&M("Warmup",this.config.warmup,Math.round(n-t),"ms",r),r}};T0=new WeakMap,k0=new WeakMap,I0=new WeakMap,d0=new WeakMap,c0=new WeakMap,P0=new WeakMap,e5=new WeakMap,N0=new WeakMap,t5=new WeakMap,o5=new WeakMap,n5=new WeakMap,r5=new WeakMap;export{a2 as Human,a2 as default}; +2Q==`; + +// package.json +var version2 = "2.1.3"; + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode; +var Human = class { + constructor(userConfig) { + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __privateAdd(this, _firstRun, void 0); + __privateAdd(this, _lastInputSum, void 0); + __privateAdd(this, _lastCacheDiff, void 0); + this.analyze = (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }; + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.tf.ENV.flags.IS_NODE && !(input instanceof tfjs_esm_exports.Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __privateAdd(this, _checkBackend, async (force = false) => { + var _a; + if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) { + const timeStamp = now(); + this.state = "backend"; + if (this.config.backend && this.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && this.config.debug) + log("running inside web worker"); + if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === "tensorflow") { + if (this.config.debug) + log("override: backend set to tensorflow while running in browser"); + this.config.backend = "humangl"; + } + if (this.tf.ENV.flags.IS_NODE && (this.config.backend === "webgl" || this.config.backend === "humangl")) { + if (this.config.debug) + log("override: backend set to webgl while running in nodejs"); + this.config.backend = "tensorflow"; + } + const available = Object.keys(this.tf.engine().registryFactory); + if (this.config.debug) + log("available backends:", available); + if (!available.includes(this.config.backend)) { + log(`error: backend ${this.config.backend} not found in registry`); + this.config.backend = this.tf.ENV.flags.IS_NODE ? "tensorflow" : "humangl"; + log(`override: using backend ${this.config.backend} instead`); + } + if (this.config.debug) + log("setting backend:", this.config.backend); + if (this.config.backend === "wasm") { + if (this.config.debug) + log("wasm path:", this.config.wasmPath); + if (typeof ((_a = this.tf) == null ? void 0 : _a.setWasmPaths) !== "undefined") + this.tf.setWasmPaths(this.config.wasmPath); + else + throw new Error("Human: WASM backend is not loaded"); + const simd = await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + const mt = await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + if (this.config.debug) + log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (this.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } + if (this.config.backend === "humangl") + register(); + try { + await this.tf.setBackend(this.config.backend); + } catch (err) { + log("error: cannot set backend:", this.config.backend, err); + } + } + this.tf.enableProdMode(); + if (this.tf.getBackend() === "webgl" || this.tf.getBackend() === "humangl") { + this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false); + this.tf.ENV.set("WEBGL_CPU_FORWARD", true); + this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV", false); + this.tf.ENV.set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (typeof this.config["deallocate"] !== "undefined" && this.config["deallocate"]) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + const gl = await this.tf.backend().getGPGPUContext().gl; + if (this.config.debug) + log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`); + } + await this.tf.ready(); + this.performance.backend = Math.trunc(now() - timeStamp); + } + }); + this.next = (result) => calc(result || this.result); + __privateAdd(this, _skipFrame, async (input) => { + if (this.config.cacheSensitivity === 0) + return false; + const resizeFact = 32; + const reduced = tfjs_esm_exports.image.resizeBilinear(input, [Math.trunc(input.shape[1] / resizeFact), Math.trunc(input.shape[2] / resizeFact)]); + const reducedData = await reduced.data(); + let sum = 0; + for (let i = 0; i < reducedData.length / 3; i++) + sum += reducedData[3 * i + 2]; + reduced.dispose(); + const diff = 100 * (Math.max(sum, __privateGet(this, _lastInputSum)) / Math.min(sum, __privateGet(this, _lastInputSum)) - 1); + __privateSet(this, _lastInputSum, sum); + const skipFrame = diff < Math.max(this.config.cacheSensitivity, __privateGet(this, _lastCacheDiff)); + __privateSet(this, _lastCacheDiff, diff > 10 * this.config.cacheSensitivity ? 0 : diff); + return skipFrame; + }); + __privateAdd(this, _warmupBitmap, async () => { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (this.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await this.detect(bitmap, this.config); + bitmap.close(); + } + return res; + }); + __privateAdd(this, _warmupCanvas, async () => new Promise((resolve) => { + let src; + let size = 0; + switch (this.config.warmup) { + case "face": + size = 256; + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + size = 1200; + src = "data:image/jpeg;base64," + body3; + break; + default: + src = null; + } + const img = new Image(); + img.onload = async () => { + const canvas2 = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(size, size) : document.createElement("canvas"); + canvas2.width = img.naturalWidth; + canvas2.height = img.naturalHeight; + const ctx = canvas2.getContext("2d"); + ctx == null ? void 0 : ctx.drawImage(img, 0, 0); + const res = await this.detect(canvas2, this.config); + resolve(res); + }; + if (src) + img.src = src; + else + resolve(null); + })); + __privateAdd(this, _warmupNode, async () => { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (this.config.warmup === "face") + img = atob(face3); + if (this.config.warmup === "body" || this.config.warmup === "full") + img = atob(body3); + if (!img) + return null; + let res; + if (typeof tfjs_esm_exports["node"] !== "undefined") { + const data2 = tfjs_esm_exports["node"].decodeJpeg(img); + const expanded = data2.expandDims(0); + this.tf.dispose(data2); + res = await this.detect(expanded, this.config); + this.tf.dispose(expanded); + } else { + if (this.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; + }); + this.config = mergeDeep(config, userConfig || {}); + this.tf = tfjs_esm_exports; + this.draw = draw_exports; + this.version = version2; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + __privateSet(this, _firstRun, true); + __privateSet(this, _lastCacheDiff, 0); + this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 }; + this.models = { + face: null, + posenet: null, + blazepose: null, + efficientpose: null, + movenet: null, + handpose: null, + age: null, + gender: null, + emotion: null, + embedding: null, + nanodet: null, + centernet: null, + faceres: null, + segmentation: null + }; + this.image = (input) => process4(input, this.config); + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + this.sysinfo = info(); + __privateSet(this, _lastInputSum, 1); + } + similarity(embedding1, embedding2) { + return similarity(embedding1, embedding2); + } + segmentation(input, background) { + return process5(input, background, this.config); + } + enhance(input) { + return enhance(input); + } + match(faceEmbedding, db, threshold = 0) { + return match(faceEmbedding, db, threshold); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (__privateGet(this, _firstRun)) { + if (this.config.debug) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version_core}`); + if (this.config.debug) + log("platform:", this.sysinfo.platform); + if (this.config.debug) + log("agent:", this.sysinfo.agent); + await __privateGet(this, _checkBackend).call(this, true); + if (this.tf.ENV.flags.IS_BROWSER) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("tf flags:", this.tf.ENV.flags); + } + } + await load13(this); + if (__privateGet(this, _firstRun)) { + if (this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + __privateSet(this, _firstRun, false); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.load || 0)) + this.performance.load = current; + } + async detect(input, userConfig) { + return new Promise(async (resolve) => { + this.state = "config"; + let timeStamp; + let elapsedTime; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + resolve({ error }); + } + const timeStart = now(); + await __privateGet(this, _checkBackend).call(this); + await this.load(); + timeStamp = now(); + let process6 = process4(input, this.config); + this.performance.image = Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (this.config.segmentation.enabled && process6 && process6.tensor) { + this.analyze("Start Segmentation:"); + this.state = "run:segmentation"; + timeStamp = now(); + await predict11(process6); + elapsedTime = Math.trunc(now() - timeStamp); + if (elapsedTime > 0) + this.performance.segmentation = elapsedTime; + if (process6.canvas) { + tfjs_esm_exports.dispose(process6.tensor); + process6 = process4(process6.canvas, this.config); + } + this.analyze("End Segmentation:"); + } + if (!process6 || !process6.tensor) { + log("could not convert input to tensor"); + resolve({ error: "could not convert input to tensor" }); + return; + } + timeStamp = now(); + this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor); + if (!this.performance.frames) + this.performance.frames = 0; + if (!this.performance.cached) + this.performance.cached = 0; + this.performance.frames++; + if (this.config.skipFrame) + this.performance.cached++; + this.performance.changed = Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes; + let bodyRes; + let handRes; + let objectRes; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + this.state = "run:face"; + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : []; + elapsedTime = Math.trunc(now() - timeStamp); + if (elapsedTime > 0) + this.performance.face = elapsedTime; + } + this.analyze("Start Body:"); + if (this.config.async) { + if (this.config.body.modelPath.includes("posenet")) + bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("movenet")) + bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : []; + if (this.performance.body) + delete this.performance.body; + } else { + this.state = "run:body"; + timeStamp = now(); + if (this.config.body.modelPath.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : []; + else if (this.config.body.modelPath.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : []; + elapsedTime = Math.trunc(now() - timeStamp); + if (elapsedTime > 0) + this.performance.body = elapsedTime; + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + if (this.config.async) { + handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + this.state = "run:hand"; + timeStamp = now(); + handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : []; + elapsedTime = Math.trunc(now() - timeStamp); + if (elapsedTime > 0) + this.performance.hand = elapsedTime; + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + if (this.config.async) { + if (this.config.object.modelPath.includes("nanodet")) + objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : []; + else if (this.config.object.modelPath.includes("centernet")) + objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + this.state = "run:object"; + timeStamp = now(); + if (this.config.object.modelPath.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : []; + else if (this.config.object.modelPath.includes("centernet")) + objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : []; + elapsedTime = Math.trunc(now() - timeStamp); + if (elapsedTime > 0) + this.performance.object = elapsedTime; + } + this.analyze("End Object:"); + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face(faceRes), ...body(bodyRes), ...hand(handRes), ...iris(faceRes)]; + if (!this.config.async) + this.performance.gesture = Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = Math.trunc(now() - timeStart); + this.state = "idle"; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: process6.canvas, + timestamp: Date.now(), + get persons() { + var _a; + return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape); + } + }; + tfjs_esm_exports.dispose(process6.tensor); + resolve(this.result); + }); + } + async warmup(userConfig) { + const t0 = now(); + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (!this.config.warmup || this.config.warmup === "none") + return { error: "null" }; + let res; + if (typeof createImageBitmap === "function") + res = await __privateGet(this, _warmupBitmap).call(this); + else if (typeof Image !== "undefined") + res = await __privateGet(this, _warmupCanvas).call(this); + else + res = await __privateGet(this, _warmupNode).call(this); + const t1 = now(); + if (this.config.debug) + log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res); + return res; + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_firstRun = new WeakMap(); +_lastInputSum = new WeakMap(); +_lastCacheDiff = new WeakMap(); +_sanity = new WeakMap(); +_checkBackend = new WeakMap(); +_skipFrame = new WeakMap(); +_warmupBitmap = new WeakMap(); +_warmupCanvas = new WeakMap(); +_warmupNode = new WeakMap(); +export { + Human, + Human 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 f6773ae7..853c8bd0 100644 --- a/dist/human.esm-nobundle.js.map +++ b/dist/human.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/helpers.ts", "../src/config.ts", "../src/sysinfo.ts", "../tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/facemesh.ts", "../src/faceres/faceres.ts", "../src/emotion/emotion.ts", "../src/posenet/keypoints.ts", "../src/posenet/utils.ts", "../src/posenet/poses.ts", "../src/posenet/posenet.ts", "../src/handpose/box.ts", "../src/handpose/anchors.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/handpose.ts", "../src/blazepose/annotations.ts", "../src/blazepose/blazepose.ts", "../src/efficientpose/efficientpose.ts", "../src/movenet/movenet.ts", "../src/object/labels.ts", "../src/object/nanodet.ts", "../src/object/centernet.ts", "../src/image/imagefx.js", "../src/image/image.ts", "../src/segmentation/segmentation.ts", "../src/models.ts", "../src/face.ts", "../src/gesture/gesture.ts", "../src/draw/draw.ts", "../src/persons.ts", "../src/interpolate.ts", "../src/sample.ts", "../src/human.ts"], - "sourcesContent": ["/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${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\n// helper function: gets elapsed time on both browser and nodejs\nexport const 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\nexport function 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\n// helper function: return min and max from input array\nexport const minmax = (data) => data.reduce((acc, val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n * @typedef Config\n */\nexport interface Config {\n /** Backend used for TFJS operations */\n backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n\n /** Path to *.wasm files if backend is set to `wasm` */\n wasmPath: string,\n\n /** Print debug statements to console */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n */\n warmup: 'none' | 'face' | 'full' | 'body',\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n */\n modelBasePath: string,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n */\n cacheSensitivity: number;\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n */\n skipFrame: boolean;\n\n /** Run input through image filters before inference\n * - image filters run with near-zero latency as they are executed on the GPU\n */\n filter: {\n enabled: boolean,\n /** Resize input width\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 */\n width: number,\n /** 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 */\n height: number,\n /** Return processed canvas imagedata in result */\n return: boolean,\n /** Flip input as mirror image */\n flip: boolean,\n /** Range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** Range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** Image negative */\n negative: boolean,\n /** Image sepia colors */\n sepia: boolean,\n /** Image vintage colors */\n vintage: boolean,\n /** Image kodachrome colors */\n kodachrome: boolean,\n /** Image technicolor colors */\n technicolor: boolean,\n /** Image polaroid camera effect */\n polaroid: boolean,\n /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n },\n // type definition end\n\n /** Controlls gesture detection */\n gesture: {\n enabled: boolean,\n },\n\n /** Controlls and configures all face-specific options:\n * - face detection, face mesh detection, age, gender, emotion detection and face description\n * Parameters:\n * - enabled: true/false\n * - modelPath: path for each of face models\n * - minConfidence: threshold for discarding a prediction\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of faces detected in the input, should be set to the minimum number for performance\n * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n * - return: return extracted face as tensor for futher user processing, in which case user is reponsible for manually disposing the tensor\n */\n face: {\n enabled: boolean,\n detector: {\n modelPath: string,\n rotation: boolean,\n maxDetected: number,\n skipFrames: number,\n minConfidence: number,\n iouThreshold: number,\n return: boolean,\n },\n mesh: {\n enabled: boolean,\n modelPath: string,\n },\n iris: {\n enabled: boolean,\n modelPath: string,\n },\n description: {\n enabled: boolean,\n modelPath: string,\n skipFrames: number,\n minConfidence: number,\n },\n emotion: {\n enabled: boolean,\n minConfidence: number,\n skipFrames: number,\n modelPath: string,\n },\n },\n\n /** Controlls and configures all body detection specific options\n * - enabled: true/false\n * - modelPath: body pose model, can be absolute path or relative to modelBasePath\n * - minConfidence: threshold for discarding a prediction\n * - maxDetected: maximum number of people detected in the input, should be set to the minimum number for performance\n */\n body: {\n enabled: boolean,\n modelPath: string,\n maxDetected: number,\n minConfidence: number,\n skipFrames: number,\n },\n\n /** Controlls and configures all hand detection specific options\n * - enabled: true/false\n * - landmarks: detect hand landmarks or just hand boundary box\n * - modelPath: paths for hand detector and hand skeleton models, can be absolute path or relative to modelBasePath\n * - minConfidence: threshold for discarding a prediction\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of hands detected in the input, should be set to the minimum number for performance\n * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n */\n hand: {\n enabled: boolean,\n rotation: boolean,\n skipFrames: number,\n minConfidence: number,\n iouThreshold: number,\n maxDetected: number,\n landmarks: boolean,\n detector: {\n modelPath: string,\n },\n skeleton: {\n modelPath: string,\n },\n },\n\n /** Controlls and configures all object detection specific options\n * - enabled: true/false\n * - modelPath: object detection model, can be absolute path or relative to modelBasePath\n * - minConfidence: minimum score that detection must have to return as valid object\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of detections to return\n */\n object: {\n enabled: boolean,\n modelPath: string,\n minConfidence: number,\n iouThreshold: number,\n maxDetected: number,\n skipFrames: number,\n },\n\n /** Controlls and configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n *\n * - enabled: true/false\n * - modelPath: object detection model, can be absolute path or relative to modelBasePath\n */\n segmentation: {\n enabled: boolean,\n modelPath: string,\n },\n}\n\nconst config: Config = {\n backend: 'webgl', // select tfjs backend to use, leave empty to use default backend\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n modelBasePath: '../models/', // base path for all models\n wasmPath: '../node_modules/@tensorflow/tfjs-backend-wasm/dist/', // path for wasm binaries, only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n warmup: 'full', // 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 // only used for `webgl` and `humangl` backends\n cacheSensitivity: 0.75, // cache sensitivity\n // values 0..1 where 0.01 means reset cache if input changed more than 1%\n // set to 0 to disable caching\n skipFrame: false, // internal & dynamic\n filter: { // run input through image filters before inference\n // image filters run with near-zero latency as they are executed on the GPU\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 flip: false, // flip input as mirror image\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 gesture recognition based on model results\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: 'blazeface.json', // detector model, can be absolute path or relative to modelBasePath\n rotation: true, // 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 // this parameter is not valid in nodejs\n maxDetected: 15, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 15, // how many max frames to go without re-running the face bounding box detector\n // only used when cacheSensitivity is not zero\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 minConfidence: 0.2, // threshold for discarding a prediction\n iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed\n return: false, // return extracted face as tensor\n // in which case user is reponsible for disposing the tensor\n },\n\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json', // facemesh model, can be absolute path or relative to modelBasePath\n },\n\n iris: {\n enabled: true,\n modelPath: 'iris.json', // face iris model\n // can be either absolute path or relative to modelBasePath\n },\n\n description: {\n enabled: true, // to improve accuracy of face description extraction it is\n // recommended to enable detector.rotation and mesh.enabled\n modelPath: 'faceres.json', // face description model\n // can be either absolute path or relative to modelBasePath\n skipFrames: 11, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n minConfidence: 0.1, // threshold for discarding a prediction\n },\n\n emotion: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 17, // how max many frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n modelPath: 'emotion.json', // face emotion model, can be absolute path or relative to modelBasePath\n },\n },\n\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json', // body model, can be absolute path or relative to modelBasePath\n // can be 'posenet', 'blazepose', 'efficientpose', 'movenet-lightning', 'movenet-thunder'\n maxDetected: 1, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as other models detects single pose\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 1, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n},\n\n hand: {\n enabled: true,\n rotation: true, // 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 skipFrames: 18, // how many max frames to go without re-running the hand bounding box detector\n // only used when cacheSensitivity is not zero\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 minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed\n maxDetected: 2, // 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: 'handdetect.json', // hand detector model, can be absolute path or relative to modelBasePath\n },\n skeleton: {\n modelPath: 'handskeleton.json', // hand skeleton model, can be absolute path or relative to modelBasePath\n },\n },\n\n object: {\n enabled: false,\n modelPath: 'mb3-centernet.json', // experimental: object detection model, can be absolute path or relative to modelBasePath\n // can be 'mb3-centernet' or 'nanodet'\n minConfidence: 0.2, // threshold for discarding a prediction\n iouThreshold: 0.4, // ammount of overlap between two detected objects before one object is removed\n maxDetected: 10, // maximum number of objects detected in the input\n skipFrames: 19, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n },\n\n segmentation: {\n enabled: false, // controlls and configures all body segmentation module\n // removes background from input containing person\n // if segmentation is enabled it will run as preprocessing task before any other model\n // alternatively leave it disabled and use it on-demand using human.segmentation method which can\n // remove background or replace it with user-provided background\n modelPath: 'selfie.json', // experimental: object detection model, can be absolute path or relative to modelBasePath\n // can be 'selfie' or 'meet'\n },\n};\nexport { config as defaults };\n", "/**\n * Helper function that returns basic system info\n */\nexport function info(): { platform: string, agent: string } {\n let platform;\n let agent;\n if (typeof navigator !== 'undefined') {\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw && raw[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n agent = navigator.userAgent.replace(raw[0], '');\n if (platform[1]) agent = agent.replace(raw[1], '');\n agent = agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n platform = `${process.platform} ${process.arch}`;\n agent = `NodeJS ${process.version}`;\n }\n return { platform, agent };\n}\n", "/**\n * Creates tfjs bundle used by Human browser build target\n * @external\n */\n\n// get versions of all packages\nimport { version as tfjsVersion } from '@tensorflow/tfjs/package.json';\nimport { version as tfjsCoreVersion } from '@tensorflow/tfjs-core/package.json';\nimport { version as tfjsDataVersion } from '@tensorflow/tfjs-data/package.json';\nimport { version as tfjsLayersVersion } from '@tensorflow/tfjs-layers/package.json';\nimport { version as tfjsConverterVersion } from '@tensorflow/tfjs-converter/package.json';\nimport { version as tfjsBackendCPUVersion } from '@tensorflow/tfjs-backend-cpu/package.json';\nimport { version as tfjsBackendWebGLVersion } from '@tensorflow/tfjs-backend-webgl/package.json';\nimport { version as tfjsBackendWASMVersion } from '@tensorflow/tfjs-backend-wasm/package.json';\n\n// export all from sources\n// requires treeShaking:ignore-annotations due to tfjs misconfiguration\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export all from build\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// export * from '@tensorflow/tfjs-backend-webgpu/dist/index.js'; // experimental\n\n// export versions\nexport const version = {\n tfjs: tfjsVersion,\n 'tfjs-core': tfjsCoreVersion,\n 'tfjs-data': tfjsDataVersion,\n 'tfjs-layers': tfjsLayersVersion,\n 'tfjs-converter': tfjsConverterVersion,\n 'tfjs-backend-cpu': tfjsBackendCPUVersion,\n 'tfjs-backend-webgl': tfjsBackendWebGLVersion,\n 'tfjs-backend-wasm': tfjsBackendWASMVersion,\n};\n", "/**\n * Custom TFJS backend for Human based on WebGL\n * Not used by default\n */\n\nimport { log } from '../helpers';\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 extensions: [],\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\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions() as string[];\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(): void {\n if (!tf.findBackend(config.name)) {\n // log('backend registration:', config.name);\n try {\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 config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\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 } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n log('backend registered:', config.name);\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,\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 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, 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 = [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)];\n const endPoint = [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function 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\nexport const disposeBox = (t) => {\n tf.dispose(t.startPoint);\n tf.dispose(t.endPoint);\n};\n\nexport const createBox = (startEndTensor) => ({\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\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\nexport function 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", "import { log, join, mergeDeep } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\nimport { Config } from '../config';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nconst keypointsCount = 6;\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 model: GraphModel;\n anchorsData: [number, number][];\n anchors: Tensor;\n inputSize: number;\n config: Config;\n\n constructor(model, config: Config) {\n this.model = model;\n this.anchorsData = util.generateAnchors(model.inputs[0].shape[1]);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = model.inputs[0].shape[2];\n this.config = config;\n }\n\n async getBoundingBoxes(inputImage: Tensor, userConfig: Config) {\n // sanity check on input\n // @ts-ignore isDisposed is internal property\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [batch, boxes, scores] = tf.tidy(() => {\n const resizedImage = tf.image.resizeBilinear(inputImage, [this.inputSize, this.inputSize]);\n const normalizedImage = tf.sub(tf.div(resizedImage, 127.5), 0.5);\n const res = this.model.execute(normalizedImage);\n let batchOut;\n if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n const sorted = res.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 batchOut = tf.squeeze(concat, 0);\n } else {\n batchOut = tf.squeeze(res); // when using tfhub model\n }\n const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n const scoresOut = tf.squeeze(tf.sigmoid(logits)).dataSync(); // inside tf.tidy\n return [batchOut, boxesOut, scoresOut];\n });\n\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n const nmsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);\n const nms = await nmsTensor.array();\n tf.dispose(nmsTensor);\n const annotatedBoxes: Array<{ box: { startPoint: Tensor, endPoint: Tensor }, landmarks: Tensor, anchor: number[], confidence: number }> = [];\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > this.config.face.detector.minConfidence) {\n const boundingBox = tf.slice(boxes, [nms[i], 0], [1, -1]);\n const localBox = box.createBox(boundingBox);\n tf.dispose(boundingBox);\n const anchor = this.anchorsData[nms[i]];\n const landmarks = tf.tidy(() => tf.reshape(tf.squeeze(tf.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));\n annotatedBoxes.push({ box: localBox, landmarks, anchor, confidence });\n }\n }\n tf.dispose(batch);\n tf.dispose(boxes);\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n };\n }\n}\n\nexport async function load(config: Config) {\n const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const blazeFace = new BlazeFaceModel(model, config);\n if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n return blazeFace;\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, 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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 * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { BlazeFaceModel } from './blazeface';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 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: Array<{ startPoint: number[], endPoint: number[], landmarks: Array, confidence: number, faceConfidence?: number }>;\n boundingBoxDetector: BlazeFaceModel; // tf.GraphModel\n meshDetector: GraphModel; // tf.GraphModel\n irisModel: GraphModel; // tf.GraphModel\n boxSize: number;\n meshSize: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n this.irisSize = irisModel?.inputs[0].shape[1] || 0;\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 coordsScaled = rawCoords.map((coord) => ([\n boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n 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 Math.round(coord[0] + util.dot(boxCenter, inverseRotationMatrix[0])),\n Math.round(coord[1] + util.dot(boxCenter, inverseRotationMatrix[1])),\n Math.round(coord[2]),\n ]));\n }\n\n // eslint-disable-next-line class-methods-use-this\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[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(bounding.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.meshSize,\n box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n box.endPoint[0] / this.meshSize,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip && tf.ENV.flags.IS_BROWSER) {\n crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\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<[number, number, number]> = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; 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(irisLandmarks.index) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n // eslint-disable-next-line class-methods-use-this\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][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.skipFrame) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input, config);\n this.skipped = 0;\n }\n if (config.skipFrame) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (!config.skipFrame || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxDetected)))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n const startPoint = await possible.box.startPoint.data();\n const endPoint = await possible.box.endPoint.data();\n const landmarks = await possible.landmarks.array();\n this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\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;\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 tf.dispose(prediction.box.startPoint);\n tf.dispose(prediction.box.endPoint);\n tf.dispose(prediction.landmarks);\n });\n }\n const 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\n if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\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); // rotateWithOffset is not defined for tfjs-node\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n if (config.face.mesh.enabled) face = tf.div(bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]), 255);\n else face = tf.div(bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]), 255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const clonedImage = input.clone();\n if (config.face.mesh.enabled) face = tf.div(bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]), 255);\n else face = tf.div(bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]), 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 mesh: [],\n box,\n faceConfidence: null,\n boxConfidence: box.confidence,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.execute(face) as Array; // The first returned tensor represents facial contours which are already included in the coordinates.\n const faceConfidence = confidence.dataSync()[0] as number; // inside tf.tidy\n if (faceConfidence < config.face.detector.minConfidence) {\n this.storedBoxes[i].confidence = faceConfidence; // reset confidence of cached box\n return null; // if below confidence just exit\n }\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, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])) as Tensor;\n const eyePredictionsData = eyePredictions.dataSync(); // inside tf.tidy\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 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', null);\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\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\n // 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 replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\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 // override box from detection with one calculated from mesh\n const mesh = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const storeConfidence = box.confidence;\n // @ts-ignore enlargeBox does not include confidence so we append it manually\n box = bounding.enlargeBox(bounding.calculateLandmarksBoundingBox(mesh), 1.5); // redefine box with mesh calculated one\n box.confidence = storeConfidence;\n\n // do rotation one more time with mesh keypoints if we want to return perfect image\n if (config.face.detector.rotation && config.face.mesh.enabled && config.face.description.enabled && tf.ENV.flags.IS_BROWSER) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\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(tf.cast(input, 'float32'), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n face = tf.div(bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]), 255);\n }\n\n const prediction = {\n mesh,\n box,\n faceConfidence,\n boxConfidence: box.confidence,\n image: face,\n };\n\n // updated stored cache values\n this.storedBoxes[i] = { ...bounding.squarifyBox(box), confidence: box.confidence, faceConfidence };\n\n return prediction;\n }));\n\n // results = results.filter((a) => a !== null);\n // remove cache entries for detected boxes on low confidence\n if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.confidence > config.face.detector.minConfidence);\n this.detectedFaces = results.length;\n\n return results;\n }\n}\n", "/**\n * FaceMesh & BlazeFace Module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Face } from '../result';\nimport { Config } from '../config';\n\nlet faceModels: [blazeface.BlazeFaceModel | null, GraphModel | null, GraphModel | null] = [null, null, null];\nlet facePipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await facePipeline.predict(input, config);\n const results: Array = [];\n let id = 0;\n for (const prediction of (predictions || [])) {\n if (!prediction || prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const meshRaw = prediction.mesh.map((pt) => [\n pt[0] / (input.shape[2] || 0),\n pt[1] / (input.shape[1] || 0),\n pt[2] / facePipeline.meshSize,\n ]);\n const annotations = {};\n if (prediction.mesh && prediction.mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => prediction.mesh[index]);\n }\n const clampedBox: [number, number, number, number] = prediction.box ? [\n Math.trunc(Math.max(0, prediction.box.startPoint[0])),\n Math.trunc(Math.max(0, prediction.box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])),\n ] : [0, 0, 0, 0];\n const boxRaw: [number, number, number, number] = prediction.box ? [\n prediction.box.startPoint[0] / (input.shape[2] || 0),\n prediction.box.startPoint[1] / (input.shape[1] || 0),\n (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),\n (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0),\n ] : [0, 0, 0, 0];\n results.push({\n id: id++,\n score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n boxScore: Math.round(100 * prediction.boxConfidence) / 100,\n faceScore: Math.round(100 * prediction.faceConfidence) / 100,\n box: clampedBox,\n boxRaw,\n mesh: prediction.mesh,\n meshRaw,\n annotations,\n tensor: prediction.image,\n });\n if (prediction.coords) tf.dispose(prediction.coords);\n }\n return results;\n}\n\nexport async function load(config): Promise<[unknown, GraphModel | null, GraphModel | null]> {\n if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n // @ts-ignore type mismatch for GraphModel\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n if (config.face.mesh.enabled) {\n if (!faceModels[1] || !faceModels[1]['modelUrl']) log('load model failed:', config.face.mesh.modelPath);\n else if (config.debug) log('load model:', faceModels[1]['modelUrl']);\n }\n if (config.face.iris.enabled) {\n if (!faceModels[2] || !faceModels[2]['modelUrl']) log('load model failed:', config.face.iris.modelPath);\n else if (config.debug) log('load model:', faceModels[2]['modelUrl']);\n }\n } else if (config.debug) {\n if (faceModels[0]) log('cached model:', faceModels[0].model['modelUrl']);\n if (faceModels[1]) log('cached model:', faceModels[1]['modelUrl']);\n if (faceModels[2]) log('cached model:', faceModels[2]['modelUrl']);\n }\n facePipeline = new facepipeline.Pipeline(faceModels[0], faceModels[1], faceModels[2]);\n return faceModels;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * HSE-FaceRes Module\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\nconst last: Array<{\n age: number,\n gender: string,\n genderScore: number,\n descriptor: number[],\n}> = [];\n\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config: Config): Promise {\n const modelUrl = join(config.modelBasePath, config.face.description.modelPath);\n if (!model) {\n // @ts-ignore type mismatch for GraphModel\n model = await tf.loadGraphModel(modelUrl);\n if (!model) log('load model failed:', config.face.description.modelPath);\n else if (config.debug) log('load model:', modelUrl);\n } else if (config.debug) log('cached model:', modelUrl);\n return model;\n}\n\nexport function similarity(embedding1: Array, embedding2: Array, order = 2): number {\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, euclidean distance is limited case where order is 2\n const distance = 5.0 * embedding1\n .map((_val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n .reduce((sum, now) => (sum + now), 0) // sum all distances\n ** (1 / order); // get root of\n const res = Math.max(0, 100 - distance) / 100.0;\n return res;\n}\n\nexport function match(embedding: Array, db: DB, threshold = 0) {\n let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n for (const f of db) {\n if (f.embedding && f.name) {\n const perc = similarity(embedding, f.embedding);\n if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n }\n }\n return best;\n}\n\nexport function enhance(input): Tensor {\n const image = tf.tidy(() => {\n // input received from detector is already normalized to 0..1\n // input is also assumed to be straightened\n const tensor = input.image || input.tensor || input;\n if (!(tensor instanceof tf.Tensor)) return null;\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n if (!model.inputs[0].shape) return null; // model has no shape so no point continuing\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n /*\n // just resize to fit the embedding model instead of cropping\n const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n */\n\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 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 const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n\n /*\n // increase image pseudo-contrast 100%\n // (or do it per-channel so mean is done on each channel)\n // (or calculate histogram and do it based on histogram)\n const mean = merge.mean();\n const factor = 2;\n const contrast = merge.sub(mean).mul(factor).add(mean);\n */\n\n /*\n // normalize brightness from 0..1\n // silly way of creating pseudo-hdr of image\n const darken = crop.sub(crop.min());\n const lighten = darken.div(darken.max());\n */\n\n const norm = tf.mul(crop, 255);\n\n return norm;\n });\n return image;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count) {\n if (!model) return null;\n if ((skipped < config.face.description.skipFrames) && config.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const enhanced = enhance(image);\n\n let resT;\n const obj = {\n age: 0,\n gender: 'unknown',\n genderScore: 0,\n descriptor: [],\n };\n\n if (config.face.description.enabled) resT = await model.predict(enhanced);\n tf.dispose(enhanced);\n\n if (resT) {\n tf.tidy(() => {\n const gender = resT.find((t) => t.shape[1] === 1).dataSync(); // inside tf.tidy\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > config.face.description.minConfidence) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const age = tf.argMax(resT.find((t) => t.shape[1] === 100), 1).dataSync()[0]; // inside tf.tidy\n const all = resT.find((t) => t.shape[1] === 100).dataSync(); // inside tf.tidy\n obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n\n obj.descriptor = [...desc.dataSync()]; // inside tf.tidy\n });\n resT.forEach((t) => tf.dispose(t));\n }\n\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * Emotion Module\n */\n\nimport { log, join } from '../helpers';\nimport { Config } from '../config';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\n// let last: Array<{ score: number, emotion: string }> = [];\nconst last: Array> = [];\nlet lastCount = 0;\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\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n tf.dispose(resize);\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 tf.dispose(red);\n tf.dispose(green);\n tf.dispose(blue);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n tf.dispose(redNorm);\n tf.dispose(greenNorm);\n tf.dispose(blueNorm);\n const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));\n tf.dispose(grayscale);\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n const data = await emotionT.data();\n tf.dispose(emotionT);\n for (let i = 0; i < data.length; i++) {\n if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n tf.dispose(normalize);\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\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 count = partNames.length; // 17 keypoints\n\nexport const partIds = 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", "import * as kpt from './keypoints';\nimport { Body } from '../result';\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\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = 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: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i) => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight],\n })),\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: Array; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\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() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\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 // @ts-ignore getter is of unknown type\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\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.count),\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 utils from './utils';\nimport * as kpt from './keypoints';\n\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: kpt.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = kpt.poseChain.map(([parentJoinName, childJoinName]) => ([kpt.partIds[parentJoinName], kpt.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: kpt.partNames[root.part.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 sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, 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 sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, 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(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.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 < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: Array<{ keypoints, box: [number, number, number, number], score: number }> = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !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 // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n", "/**\n * PoseNet module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as poses from './poses';\nimport * as util from './utils';\nimport { Body } from '../result';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Array = model.execute(normalized, poseNetOutputs) as Array;\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = results3d[1].sigmoid(); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = await poses.decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = util.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as Body[];\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch for GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\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", "export const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 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0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.609375, y: 0.921875 },\n { x: 0.609375, y: 0.921875 },\n { x: 0.640625, y: 0.921875 },\n { x: 0.640625, y: 0.921875 },\n { x: 0.671875, y: 0.921875 },\n { x: 0.671875, y: 0.921875 },\n { x: 0.703125, y: 0.921875 },\n { x: 0.703125, y: 0.921875 },\n { x: 0.734375, y: 0.921875 },\n { x: 0.734375, y: 0.921875 },\n { x: 0.765625, y: 0.921875 },\n { x: 0.765625, y: 0.921875 },\n { x: 0.796875, y: 0.921875 },\n { x: 0.796875, y: 0.921875 },\n { x: 0.828125, y: 0.921875 },\n { x: 0.828125, y: 0.921875 },\n { x: 0.859375, y: 0.921875 },\n { x: 0.859375, y: 0.921875 },\n { x: 0.890625, y: 0.921875 },\n { x: 0.890625, y: 0.921875 },\n { x: 0.921875, y: 0.921875 },\n { x: 0.921875, y: 0.921875 },\n { x: 0.953125, y: 0.921875 },\n { x: 0.953125, y: 0.921875 },\n { x: 0.984375, y: 0.921875 },\n { x: 0.984375, y: 0.921875 },\n { x: 0.015625, y: 0.953125 },\n { x: 0.015625, y: 0.953125 },\n { x: 0.046875, y: 0.953125 },\n { x: 0.046875, y: 0.953125 },\n { x: 0.078125, y: 0.953125 },\n { x: 0.078125, y: 0.953125 },\n { x: 0.109375, y: 0.953125 },\n { x: 0.109375, y: 0.953125 },\n { x: 0.140625, y: 0.953125 },\n { x: 0.140625, y: 0.953125 },\n { x: 0.171875, y: 0.953125 },\n { x: 0.171875, y: 0.953125 },\n { x: 0.203125, y: 0.953125 },\n { x: 0.203125, y: 0.953125 },\n { x: 0.234375, y: 0.953125 },\n { x: 0.234375, y: 0.953125 },\n { x: 0.265625, y: 0.953125 },\n { x: 0.265625, y: 0.953125 },\n { x: 0.296875, y: 0.953125 },\n { x: 0.296875, y: 0.953125 },\n { x: 0.328125, y: 0.953125 },\n { x: 0.328125, y: 0.953125 },\n { x: 0.359375, y: 0.953125 },\n { x: 0.359375, y: 0.953125 },\n { x: 0.390625, y: 0.953125 },\n { x: 0.390625, y: 0.953125 },\n { x: 0.421875, y: 0.953125 },\n { x: 0.421875, y: 0.953125 },\n { x: 0.453125, y: 0.953125 },\n { x: 0.453125, y: 0.953125 },\n { x: 0.484375, y: 0.953125 },\n { x: 0.484375, y: 0.953125 },\n { x: 0.515625, y: 0.953125 },\n { x: 0.515625, y: 0.953125 },\n { x: 0.546875, y: 0.953125 },\n { x: 0.546875, y: 0.953125 },\n { x: 0.578125, y: 0.953125 },\n { x: 0.578125, y: 0.953125 },\n { x: 0.609375, y: 0.953125 },\n { x: 0.609375, y: 0.953125 },\n { x: 0.640625, y: 0.953125 },\n { x: 0.640625, y: 0.953125 },\n { x: 0.671875, y: 0.953125 },\n { x: 0.671875, y: 0.953125 },\n { x: 0.703125, y: 0.953125 },\n { x: 0.703125, y: 0.953125 },\n { x: 0.734375, y: 0.953125 },\n { x: 0.734375, y: 0.953125 },\n { x: 0.765625, y: 0.953125 },\n { x: 0.765625, y: 0.953125 },\n { x: 0.796875, y: 0.953125 },\n { x: 0.796875, y: 0.953125 },\n { x: 0.828125, y: 0.953125 },\n { x: 0.828125, y: 0.953125 },\n { x: 0.859375, y: 0.953125 },\n { x: 0.859375, y: 0.953125 },\n { x: 0.890625, y: 0.953125 },\n { x: 0.890625, y: 0.953125 },\n { x: 0.921875, y: 0.953125 },\n { x: 0.921875, y: 0.953125 },\n { x: 0.953125, y: 0.953125 },\n { x: 0.953125, y: 0.953125 },\n { x: 0.984375, y: 0.953125 },\n { x: 0.984375, y: 0.953125 },\n { x: 0.015625, y: 0.984375 },\n { x: 0.015625, y: 0.984375 },\n { x: 0.046875, y: 0.984375 },\n { x: 0.046875, y: 0.984375 },\n { x: 0.078125, y: 0.984375 },\n { x: 0.078125, y: 0.984375 },\n { x: 0.109375, y: 0.984375 },\n { x: 0.109375, y: 0.984375 },\n { x: 0.140625, y: 0.984375 },\n { x: 0.140625, y: 0.984375 },\n { x: 0.171875, y: 0.984375 },\n { x: 0.171875, y: 0.984375 },\n { x: 0.203125, y: 0.984375 },\n { x: 0.203125, y: 0.984375 },\n { x: 0.234375, y: 0.984375 },\n { x: 0.234375, y: 0.984375 },\n { x: 0.265625, y: 0.984375 },\n { x: 0.265625, y: 0.984375 },\n { x: 0.296875, y: 0.984375 },\n { x: 0.296875, y: 0.984375 },\n { x: 0.328125, y: 0.984375 },\n { x: 0.328125, y: 0.984375 },\n { x: 0.359375, y: 0.984375 },\n { x: 0.359375, y: 0.984375 },\n { x: 0.390625, y: 0.984375 },\n { x: 0.390625, y: 0.984375 },\n { x: 0.421875, y: 0.984375 },\n { x: 0.421875, y: 0.984375 },\n { x: 0.453125, y: 0.984375 },\n { x: 0.453125, y: 0.984375 },\n { x: 0.484375, y: 0.984375 },\n { x: 0.484375, y: 0.984375 },\n { x: 0.515625, y: 0.984375 },\n { x: 0.515625, y: 0.984375 },\n { x: 0.546875, y: 0.984375 },\n { x: 0.546875, y: 0.984375 },\n { x: 0.578125, y: 0.984375 },\n { x: 0.578125, y: 0.984375 },\n { x: 0.609375, y: 0.984375 },\n { x: 0.609375, y: 0.984375 },\n { x: 0.640625, y: 0.984375 },\n { x: 0.640625, y: 0.984375 },\n { x: 0.671875, y: 0.984375 },\n { x: 0.671875, y: 0.984375 },\n { x: 0.703125, y: 0.984375 },\n { x: 0.703125, y: 0.984375 },\n { x: 0.734375, y: 0.984375 },\n { x: 0.734375, y: 0.984375 },\n { x: 0.765625, y: 0.984375 },\n { x: 0.765625, y: 0.984375 },\n { x: 0.796875, y: 0.984375 },\n { x: 0.796875, y: 0.984375 },\n { x: 0.828125, y: 0.984375 },\n { x: 0.828125, y: 0.984375 },\n { x: 0.859375, y: 0.984375 },\n { x: 0.859375, y: 0.984375 },\n { x: 0.890625, y: 0.984375 },\n { x: 0.890625, y: 0.984375 },\n { x: 0.921875, y: 0.984375 },\n { x: 0.921875, y: 0.984375 },\n { x: 0.953125, y: 0.984375 },\n { x: 0.953125, y: 0.984375 },\n { x: 0.984375, y: 0.984375 },\n { x: 0.984375, y: 0.984375 },\n { x: 0.03125, y: 0.03125 },\n { x: 0.03125, y: 0.03125 },\n { x: 0.09375, y: 0.03125 },\n { x: 0.09375, y: 0.03125 },\n { x: 0.15625, y: 0.03125 },\n { x: 0.15625, y: 0.03125 },\n { x: 0.21875, y: 0.03125 },\n { x: 0.21875, y: 0.03125 },\n { x: 0.28125, y: 0.03125 },\n { x: 0.28125, y: 0.03125 },\n { x: 0.34375, y: 0.03125 },\n { x: 0.34375, y: 0.03125 },\n { x: 0.40625, y: 0.03125 },\n { x: 0.40625, y: 0.03125 },\n { x: 0.46875, y: 0.03125 },\n { x: 0.46875, y: 0.03125 },\n { x: 0.53125, y: 0.03125 },\n { x: 0.53125, y: 0.03125 },\n { x: 0.59375, y: 0.03125 },\n { x: 0.59375, y: 0.03125 },\n { x: 0.65625, y: 0.03125 },\n { x: 0.65625, y: 0.03125 },\n { x: 0.71875, y: 0.03125 },\n { x: 0.71875, y: 0.03125 },\n { x: 0.78125, y: 0.03125 },\n { x: 0.78125, y: 0.03125 },\n { x: 0.84375, y: 0.03125 },\n { 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0.90625, y: 0.09375 },\n { x: 0.90625, y: 0.09375 },\n { x: 0.96875, y: 0.09375 },\n { x: 0.96875, y: 0.09375 },\n { x: 0.03125, y: 0.15625 },\n { x: 0.03125, y: 0.15625 },\n { x: 0.09375, y: 0.15625 },\n { x: 0.09375, y: 0.15625 },\n { x: 0.15625, y: 0.15625 },\n { x: 0.15625, y: 0.15625 },\n { x: 0.21875, y: 0.15625 },\n { x: 0.21875, y: 0.15625 },\n { x: 0.28125, y: 0.15625 },\n { x: 0.28125, y: 0.15625 },\n { x: 0.34375, y: 0.15625 },\n { x: 0.34375, y: 0.15625 },\n { x: 0.40625, y: 0.15625 },\n { x: 0.40625, y: 0.15625 },\n { x: 0.46875, y: 0.15625 },\n { x: 0.46875, y: 0.15625 },\n { x: 0.53125, y: 0.15625 },\n { x: 0.53125, y: 0.15625 },\n { x: 0.59375, y: 0.15625 },\n { x: 0.59375, y: 0.15625 },\n { x: 0.65625, y: 0.15625 },\n { x: 0.65625, y: 0.15625 },\n { x: 0.71875, y: 0.15625 },\n { x: 0.71875, y: 0.15625 },\n { x: 0.78125, y: 0.15625 },\n { x: 0.78125, y: 0.15625 },\n { x: 0.84375, y: 0.15625 },\n { x: 0.84375, y: 0.15625 },\n { x: 0.90625, y: 0.15625 },\n { x: 0.90625, y: 0.15625 },\n { x: 0.96875, y: 0.15625 },\n { x: 0.96875, y: 0.15625 },\n { x: 0.03125, y: 0.21875 },\n { x: 0.03125, y: 0.21875 },\n { x: 0.09375, y: 0.21875 },\n { x: 0.09375, y: 0.21875 },\n { x: 0.15625, y: 0.21875 },\n { x: 0.15625, y: 0.21875 },\n { x: 0.21875, y: 0.21875 },\n { x: 0.21875, y: 0.21875 },\n { x: 0.28125, y: 0.21875 },\n { x: 0.28125, y: 0.21875 },\n { x: 0.34375, y: 0.21875 },\n { x: 0.34375, y: 0.21875 },\n { x: 0.40625, y: 0.21875 },\n { x: 0.40625, y: 0.21875 },\n { x: 0.46875, y: 0.21875 },\n { x: 0.46875, y: 0.21875 },\n { x: 0.53125, y: 0.21875 },\n { x: 0.53125, y: 0.21875 },\n { x: 0.59375, y: 0.21875 },\n { x: 0.59375, y: 0.21875 },\n { x: 0.65625, y: 0.21875 },\n { x: 0.65625, y: 0.21875 },\n { x: 0.71875, y: 0.21875 },\n { x: 0.71875, y: 0.21875 },\n { x: 0.78125, y: 0.21875 },\n { x: 0.78125, y: 0.21875 },\n { x: 0.84375, y: 0.21875 },\n { x: 0.84375, y: 0.21875 },\n { x: 0.90625, y: 0.21875 },\n { x: 0.90625, y: 0.21875 },\n { x: 0.96875, y: 0.21875 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y: 0.40625 },\n { x: 0.09375, y: 0.40625 },\n { x: 0.09375, y: 0.40625 },\n { x: 0.15625, y: 0.40625 },\n { x: 0.15625, y: 0.40625 },\n { x: 0.21875, y: 0.40625 },\n { x: 0.21875, y: 0.40625 },\n { x: 0.28125, y: 0.40625 },\n { x: 0.28125, y: 0.40625 },\n { x: 0.34375, y: 0.40625 },\n { x: 0.34375, y: 0.40625 },\n { x: 0.40625, y: 0.40625 },\n { x: 0.40625, y: 0.40625 },\n { x: 0.46875, y: 0.40625 },\n { x: 0.46875, y: 0.40625 },\n { x: 0.53125, y: 0.40625 },\n { x: 0.53125, y: 0.40625 },\n { x: 0.59375, y: 0.40625 },\n { x: 0.59375, y: 0.40625 },\n { x: 0.65625, y: 0.40625 },\n { x: 0.65625, y: 0.40625 },\n { x: 0.71875, y: 0.40625 },\n { x: 0.71875, y: 0.40625 },\n { x: 0.78125, y: 0.40625 },\n { x: 0.78125, y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 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y: 0.59375 },\n { x: 0.21875, y: 0.59375 },\n { x: 0.21875, y: 0.59375 },\n { x: 0.28125, y: 0.59375 },\n { x: 0.28125, y: 0.59375 },\n { x: 0.34375, y: 0.59375 },\n { x: 0.34375, y: 0.59375 },\n { x: 0.40625, y: 0.59375 },\n { x: 0.40625, y: 0.59375 },\n { x: 0.46875, y: 0.59375 },\n { x: 0.46875, y: 0.59375 },\n { x: 0.53125, y: 0.59375 },\n { x: 0.53125, y: 0.59375 },\n { x: 0.59375, y: 0.59375 },\n { x: 0.59375, y: 0.59375 },\n { x: 0.65625, y: 0.59375 },\n { x: 0.65625, y: 0.59375 },\n { x: 0.71875, y: 0.59375 },\n { x: 0.71875, y: 0.59375 },\n { x: 0.78125, y: 0.59375 },\n { x: 0.78125, y: 0.59375 },\n { x: 0.84375, y: 0.59375 },\n { x: 0.84375, y: 0.59375 },\n { x: 0.90625, y: 0.59375 },\n { x: 0.90625, y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 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y: 0.78125 },\n { x: 0.34375, y: 0.78125 },\n { x: 0.34375, y: 0.78125 },\n { x: 0.40625, y: 0.78125 },\n { x: 0.40625, y: 0.78125 },\n { x: 0.46875, y: 0.78125 },\n { x: 0.46875, y: 0.78125 },\n { x: 0.53125, y: 0.78125 },\n { x: 0.53125, y: 0.78125 },\n { x: 0.59375, y: 0.78125 },\n { x: 0.59375, y: 0.78125 },\n { x: 0.65625, y: 0.78125 },\n { x: 0.65625, y: 0.78125 },\n { x: 0.71875, y: 0.78125 },\n { x: 0.71875, y: 0.78125 },\n { x: 0.78125, y: 0.78125 },\n { x: 0.78125, y: 0.78125 },\n { x: 0.84375, y: 0.78125 },\n { x: 0.84375, y: 0.78125 },\n { x: 0.90625, y: 0.78125 },\n { x: 0.90625, y: 0.78125 },\n { x: 0.96875, y: 0.78125 },\n { x: 0.96875, y: 0.78125 },\n { x: 0.03125, y: 0.84375 },\n { x: 0.03125, y: 0.84375 },\n { x: 0.09375, y: 0.84375 },\n { x: 0.09375, y: 0.84375 },\n { x: 0.15625, y: 0.84375 },\n { x: 0.15625, y: 0.84375 },\n { x: 0.21875, y: 0.84375 },\n { x: 0.21875, y: 0.84375 },\n { x: 0.28125, y: 0.84375 },\n { x: 0.28125, y: 0.84375 },\n { x: 0.34375, y: 0.84375 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0.40625, y: 0.90625 },\n { x: 0.40625, y: 0.90625 },\n { x: 0.46875, y: 0.90625 },\n { x: 0.46875, y: 0.90625 },\n { x: 0.53125, y: 0.90625 },\n { x: 0.53125, y: 0.90625 },\n { x: 0.59375, y: 0.90625 },\n { x: 0.59375, y: 0.90625 },\n { x: 0.65625, y: 0.90625 },\n { x: 0.65625, y: 0.90625 },\n { x: 0.71875, y: 0.90625 },\n { x: 0.71875, y: 0.90625 },\n { x: 0.78125, y: 0.90625 },\n { x: 0.78125, y: 0.90625 },\n { x: 0.84375, y: 0.90625 },\n { x: 0.84375, y: 0.90625 },\n { x: 0.90625, y: 0.90625 },\n { x: 0.90625, y: 0.90625 },\n { x: 0.96875, y: 0.90625 },\n { x: 0.96875, y: 0.90625 },\n { x: 0.03125, y: 0.96875 },\n { x: 0.03125, y: 0.96875 },\n { x: 0.09375, y: 0.96875 },\n { x: 0.09375, y: 0.96875 },\n { x: 0.15625, y: 0.96875 },\n { x: 0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n { x: 0.3125, y: 0.0625 },\n { x: 0.3125, y: 0.0625 },\n { x: 0.3125, y: 0.0625 },\n { x: 0.3125, y: 0.0625 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0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.4375, y: 0.3125 },\n { x: 0.5625, y: 0.3125 },\n { x: 0.5625, y: 0.3125 },\n { x: 0.5625, y: 0.3125 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},\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as anchors from './anchors';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n // @ts-ignore model is not undefined here\n this.inputSize = this.model?.inputs[0].shape[2];\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.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(tf.reshape(rawPalmLandmarks, [-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) as Tensor;\n const predictions = tf.squeeze(batched);\n tf.dispose(batched);\n const scoresT = tf.tidy(() => tf.squeeze(tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1]))));\n const scores = await scoresT.data();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n tf.dispose(rawBoxes);\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n const filtered = await filteredT.array();\n\n tf.dispose(scoresT);\n tf.dispose(filteredT);\n const hands: Array<{ box: Tensor, palmLandmarks: Tensor, 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(() => tf.reshape(this.normalizeLandmarks(rawPalmLandmarks, index), [-1, 2]));\n tf.dispose(rawPalmLandmarks);\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n tf.dispose(predictions);\n tf.dispose(boxes);\n return hands;\n }\n\n async estimateHandBounds(input, config): Promise<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }[]> {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => tf.sub(tf.div(tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]), 127.5), 1));\n const predictions = await this.getBoxes(image, config);\n tf.dispose(image);\n const hands: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = await prediction.box.data();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = await prediction.palmLandmarks.array();\n tf.dispose(prediction.box);\n tf.dispose(prediction.palmLandmarks);\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.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';\nimport * as detector from './handdetector';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number } | null>;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n // @ts-ignore model is not undefined here\n this.inputSize = this.handPoseModel?.inputs[0].shape[2];\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\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 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(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[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 Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(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\n // console.log(this.skipped, config.hand.skipFrames, !config.hand.landmarks, !config.skipFrame);\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.skipFrame) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.skipFrame) 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.maxDetected) || !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<{ landmarks?: number[], confidence: number, box: { topLeft: number[], bottomRight: number[] } }> = [];\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[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 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.ENV.flags.IS_BROWSER ? 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 = tf.div(croppedInput, 255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage) as Array;\n tf.dispose(handImage);\n const confidence = confidenceT.dataSync()[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\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 tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), handBoxEnlargeFactor);\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", "/**\n * HandPose module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport { Hand } from '../result';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nconst meshAnnotations = {\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\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n // @ts-ignore landmarks are not undefined\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n\n const keypoints = predictions[i].landmarks as unknown as Array<[number, number, number]>;\n\n let box: [number, number, number, number] = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: [number, number, number, number] = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!handDetectorModel || !handPoseModel) {\n // @ts-ignore type mismatch on GraphModel\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n if (config.hand.enabled) {\n if (!handDetectorModel || !handDetectorModel['modelUrl']) log('load model failed:', config.hand.detector.modelPath);\n else if (config.debug) log('load model:', handDetectorModel['modelUrl']);\n if (!handPoseModel || !handPoseModel['modelUrl']) log('load model failed:', config.hand.skeleton.modelPath);\n else if (config.debug) log('load model:', handPoseModel['modelUrl']);\n }\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = new handdetector.HandDetector(handDetectorModel);\n handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "export const full = [\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 const upper = [\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 'left:15',\n 'right:16',\n 'left:17',\n 'right:18',\n 'left:19',\n 'right:20',\n 'left:21',\n 'right:22',\n 'leftChest',\n 'rightChest',\n 'neck',\n 'forehead',\n 'left:27',\n 'right:28',\n 'left:29',\n 'right:30',\n];\n", "/**\n * BlazePose Module\n */\n\n// paper: https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as annotations from './annotations';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Body } from '../result';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch for Graphmodel\n model = await tf.loadGraphModel(join(config.modelBasePath, 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 (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if (!model) return [];\n if (!config.body.enabled) return [];\n const imgSize = { width: (image.shape[2] || 0), height: (image.shape[1] || 0) };\n const resize = tf.image.resizeBilinear(image, [model['width'], model['height']], false);\n const normalize = tf.div(resize, [255.0]);\n tf.dispose(resize);\n const resT = await model.predict(normalize) as Array;\n const findT = resT.find((t) => (t.size === 195 || t.size === 155));\n const points = await findT?.data() || []; // order of output tensors may change between models, full has 195 and upper has 155 items\n resT.forEach((t) => tf.dispose(t));\n tf.dispose(normalize);\n const keypoints: Array<{ id, part, position: [number, number, number], positionRaw: [number, number, number], score, presence }> = [];\n const labels = points?.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: [\n Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n ],\n positionRaw: [\n points[depth * i + 0] / 255, // return x value normalized to 0..1\n points[depth * i + 1] / 255, // return y value normalized to 0..1\n points[depth * i + 2] + 0, // fix negative zero\n ],\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n const box: [number, number, number, number] = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...x),\n ];\n const boxRaw: [number, number, number, number] = [0, 0, 0, 0]; // not yet implemented\n const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n return [{ id: 0, score, box, boxRaw, keypoints }];\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Body } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\ntype Keypoints = { score: number, part: string, position: [number, number], positionRaw: [number, number] };\n\nconst keypoints: Array = [];\nlet box: [number, number, number, number] = [0, 0, 0, 0];\nlet boxRaw: [number, number, number, number] = [0, 0, 0, 0];\nlet score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n const [width, height] = inputs.shape;\n return tf.tidy(() => {\n // modulus op implemented in tf\n const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n // combine all data\n const reshaped = tf.reshape(inputs, [height * width]);\n // get highest score\n const newScore = tf.max(reshaped, 0).dataSync()[0]; // inside tf.tidy\n if (newScore > minScore) {\n // skip coordinate calculation is score is too low\n const coords = tf.argMax(reshaped, 0);\n const x = mod(coords, width).dataSync()[0]; // inside tf.tidy\n const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0]; // inside tf.tidy\n return [x, y, newScore];\n }\n return [0, 0, newScore];\n });\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {\n skipped++;\n return [{ id: 0, score, box, boxRaw, keypoints }];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const enhance = tf.mul(resize, 2);\n const norm = enhance.sub(1);\n return norm;\n });\n\n let resT;\n if (config.body.enabled) resT = await model.predict(tensor);\n tf.dispose(tensor);\n\n if (resT) {\n keypoints.length = 0;\n const squeeze = resT.squeeze();\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = squeeze.unstack(2);\n tf.dispose(squeeze);\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = max2d(stack[id], config.body.minConfidence);\n if (score > config.body.minConfidence) {\n keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: bodyParts[id],\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = keypoints.map((a) => a.positionRaw[0]);\n const yRaw = keypoints.map((a) => a.positionRaw[1]);\n boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n resolve([{ id: 0, score, box, boxRaw, keypoints }]);\n });\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Body } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\ntype Keypoints = { score: number, part: string, position: [number, number], positionRaw: [number, number] };\n\nconst keypoints: Array = [];\nlet box: [number, number, number, number] = [0, 0, 0, 0];\nlet boxRaw: [number, number, number, number] = [0, 0, 0, 0];\nlet score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder', 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist', 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle'];\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {\n skipped++;\n return [{ id: 0, score, box, boxRaw, keypoints }];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const cast = tf.cast(resize, 'int32');\n return cast;\n });\n\n let resT;\n if (config.body.enabled) resT = await model.predict(tensor);\n tf.dispose(tensor);\n\n if (resT) {\n keypoints.length = 0;\n const res = await resT.array();\n tf.dispose(resT);\n const kpt = res[0][0];\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: bodyParts[id],\n positionRaw: [ // normalized to 0..1\n kpt[id][1],\n kpt[id][0],\n ],\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * kpt[id][1]),\n Math.round((image.shape[1] || 0) * kpt[id][0]),\n ],\n });\n }\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = keypoints.map((a) => a.positionRaw[0]);\n const yRaw = keypoints.map((a) => a.positionRaw[1]);\n boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n resolve([{ id: 0, score, box, boxRaw, keypoints }]);\n });\n}\n", "/**\n * CoCo Labels used by object detection modules\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * NanoDet object detection module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { labels } from './labels';\nimport { Item } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model;\nlet last: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n const inputs = Object.values(model.modelSignature['inputs']);\n model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n let id = 0;\n let results: Array = [];\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > config.object.minConfidence && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: (box.map((a) => Math.trunc(a))) as [number, number, number, number],\n boxRaw: boxRaw as [number, number, number, number],\n };\n results.push(result);\n }\n }\n }\n });\n }\n // deallocate tensors\n res.forEach((t) => tf.dispose(t));\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: Array = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = await nms.data();\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2], image.shape[1]];\n const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n const norm = tf.div(resize, 255);\n const transpose = norm.transpose([0, 3, 1, 2]);\n tf.dispose(norm);\n tf.dispose(resize);\n\n let objectT;\n if (config.object.enabled) objectT = await model.predict(transpose);\n tf.dispose(transpose);\n\n const obj = await process(objectT, model.inputSize, outputSize, config);\n last = obj;\n resolve(obj);\n });\n}\n", "/**\n * CenterNet object detection module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { labels } from './labels';\nimport { Item } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model;\nlet last: Item[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n const inputs = Object.values(model.modelSignature['inputs']);\n model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nasync function process(res: Tensor, inputSize, outputShape, config: Config) {\n if (!res) return [];\n const results: Array = [];\n const detections = await res.array();\n const squeezeT = tf.squeeze(res);\n tf.dispose(res);\n const arr = tf.split(squeezeT, 6, 1); // x1, y1, x2, y2, score, class\n tf.dispose(squeezeT);\n const stackT = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n const boxesT = tf.squeeze(stackT);\n const scoresT = tf.squeeze(arr[4]);\n const classesT = tf.squeeze(arr[5]);\n arr.forEach((t) => tf.dispose(t));\n const nmsT = await tf.image.nonMaxSuppressionAsync(boxesT, scoresT, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n tf.dispose(boxesT);\n tf.dispose(scoresT);\n tf.dispose(classesT);\n const nms = await nmsT.data();\n tf.dispose(nmsT);\n let i = 0;\n for (const id of nms) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n const label = labels[classVal].label;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ] as [number, number, number, number];\n const box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ] as [number, number, number, number];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2], input.shape[1]];\n const resize = tf.image.resizeBilinear(input, [model.inputSize, model.inputSize]);\n const objectT = config.object.enabled ? model.execute(resize, ['tower_0/detections']) : null;\n tf.dispose(resize);\n\n const obj = await process(objectT, model.inputSize, outputSize, config);\n last = obj;\n resolve(obj);\n });\n}\n", "/*\nWebGLImageFilter by Dominic Szablewski: \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)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\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)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\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 _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 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 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", "/**\n * Image Processing module used by Human\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport { Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\ntype Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\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: Input, config: Config): { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n let tensor;\n if (!input) throw new Error('Human: Input is missing');\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('Human: Input type is not recognized');\n }\n if (input instanceof tf.Tensor) {\n // if input is tensor, use as-is\n if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n } else {\n // check if resizing will be needed\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 if (!originalWidth || !originalHeight) return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = targetWidth * originalHeight / originalWidth;\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = targetHeight * originalWidth / originalHeight;\n }\n\n // create our canvas and resize it if needed\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) throw new Error('Human: Input cannot determine dimension');\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\n // draw input to our canvas\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) {\n ctx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof ctx.translate !== 'undefined') {\n ctx.translate(originalWidth, 0);\n ctx.scale(-1, 1);\n ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n ctx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n }\n }\n\n // imagefx transforms using gl\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 { tensor: null, canvas: 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 const shape = [outCanvas.height, outCanvas.width, 3];\n const pixels = tf.tensor3d(outCanvas.data, shape, 'float32');\n tensor = tf.expandDims(pixels, 0);\n tf.dispose(pixels);\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n\n // create tensor from image if tensor is not already defined\n if (!tensor) {\n let pixels;\n if (outCanvas.data) { // if we have data, just convert to tensor\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n pixels = tf.browser ? tf.browser.fromPixels(outCanvas) : null;\n } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n // we cant use canvas as-is as it already has a context, 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 pixels = tf.browser ? tf.browser.fromPixels(tempCanvas) : null;\n } else { // cpu and wasm kernel does not implement efficient fromPixels method\n // we cant use canvas as-is as it already has a context, so we do a silly one more canvas and do fromPixels on ImageData instead\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 ? tf.browser.fromPixels(data) : null;\n }\n if (pixels) {\n const casted = tf.cast(pixels, 'float32');\n tensor = tf.expandDims(casted, 0);\n tf.dispose(pixels);\n tf.dispose(casted);\n }\n }\n }\n const canvas = config.filter.return ? outCanvas : null;\n return { tensor, canvas };\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\ntype Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.segmentation.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.segmentation.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement }): Promise {\n const width = input.tensor?.shape[1] || 0;\n const height = input.tensor?.shape[2] || 0;\n if (!input.tensor) return null;\n if (!model || !model.inputs[0].shape) return null;\n const resizeInput = tf.image.resizeBilinear(input.tensor, [model.inputs[0].shape[1], model.inputs[0].shape[2]], false);\n const norm = tf.div(resizeInput, 255);\n const res = model.predict(norm) as Tensor;\n // meet output: 1,256,256,1\n // selfie output: 1,144,256,2\n tf.dispose(resizeInput);\n tf.dispose(norm);\n\n const squeeze = tf.squeeze(res, 0);\n let resizeOutput;\n if (squeeze.shape[2] === 2) {\n // model meet has two channels for fg and bg\n const softmax = squeeze.softmax();\n const [bg, fg] = tf.unstack(softmax, 2);\n const expand = tf.expandDims(fg, 2);\n const pad = tf.expandDims(expand, 0);\n tf.dispose(softmax);\n tf.dispose(bg);\n tf.dispose(fg);\n // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n const crop = tf.image.cropAndResize(pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n // otherwise run softmax after unstack and use standard resize\n // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n resizeOutput = tf.squeeze(crop, 0);\n tf.dispose(crop);\n tf.dispose(expand);\n tf.dispose(pad);\n } else { // model selfie has a single channel that we can use directly\n resizeOutput = tf.image.resizeBilinear(squeeze, [width, height]);\n }\n\n if (typeof document === 'undefined') return resizeOutput.data(); // we're running in nodejs so return alpha array as-is\n\n const overlay = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas');\n overlay.width = width;\n overlay.height = height;\n if (tf.browser) await tf.browser.toPixels(resizeOutput, overlay);\n tf.dispose(resizeOutput);\n tf.dispose(squeeze);\n tf.dispose(res);\n\n // get alpha channel data\n const alphaCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas'); // need one more copy since input may already have gl context so 2d context fails\n alphaCanvas.width = width;\n alphaCanvas.height = height;\n const ctxAlpha = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctxAlpha.filter = 'blur(8px';\n await ctxAlpha.drawImage(overlay, 0, 0);\n const alpha = ctxAlpha.getImageData(0, 0, width, height).data;\n\n // get original canvas merged with overlay\n const original = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas'); // need one more copy since input may already have gl context so 2d context fails\n original.width = width;\n original.height = height;\n const ctx = original.getContext('2d') as CanvasRenderingContext2D;\n if (input.canvas) await ctx.drawImage(input.canvas, 0, 0);\n // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n ctx.globalCompositeOperation = 'darken';\n ctx.filter = 'blur(8px)'; // use css filter for bluring, can be done with gaussian blur manually instead\n await ctx.drawImage(overlay, 0, 0);\n ctx.globalCompositeOperation = 'source-over'; // reset\n ctx.filter = 'none'; // reset\n\n input.canvas = original;\n\n return alpha;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config): Promise {\n if (busy) return null;\n busy = true;\n if (!model) await load(config);\n const img = image.process(input, config);\n const alpha = await predict(img);\n tf.dispose(img.tensor);\n\n if (background && alpha) {\n const tmp = image.process(background, config);\n const bg = tmp.canvas;\n tf.dispose(tmp.tensor);\n const fg = img.canvas;\n const fgData = fg.getContext('2d')?.getImageData(0, 0, fg.width, fg.height).data as Uint8ClampedArray;\n\n const c = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(fg.width, fg.height) : document.createElement('canvas');\n c.width = fg.width;\n c.height = fg.height;\n const ctx = c.getContext('2d') as CanvasRenderingContext2D;\n\n ctx.globalCompositeOperation = 'copy'; // reset\n ctx.drawImage(bg, 0, 0, c.width, c.height);\n const cData = ctx.getImageData(0, 0, c.width, c.height) as ImageData;\n for (let i = 0; i < c.width * c.height; i++) { // this should be done with globalCompositeOperation instead of looping through image data\n cData.data[4 * i + 0] = ((255 - alpha[4 * i + 0]) / 255.0 * cData.data[4 * i + 0]) + (alpha[4 * i + 0] / 255.0 * fgData[4 * i + 0]);\n cData.data[4 * i + 1] = ((255 - alpha[4 * i + 1]) / 255.0 * cData.data[4 * i + 1]) + (alpha[4 * i + 1] / 255.0 * fgData[4 * i + 1]);\n cData.data[4 * i + 2] = ((255 - alpha[4 * i + 2]) / 255.0 * cData.data[4 * i + 2]) + (alpha[4 * i + 2] / 255.0 * fgData[4 * i + 2]);\n cData.data[4 * i + 3] = ((255 - alpha[4 * i + 3]) / 255.0 * cData.data[4 * i + 3]) + (alpha[4 * i + 3] / 255.0 * fgData[4 * i + 3]);\n }\n ctx.putImageData(cData, 0, 0);\n img.canvas = c;\n }\n busy = false;\n return img.canvas;\n}\n", "import * as facemesh from './blazeface/facemesh';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as movenet from './movenet/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as centernet from './object/centernet';\nimport * as segmentation from './segmentation/segmentation';\n// import * as agegenderrace from './gear/agegenderrace';\n\n/** Load method preloads all instance.configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n * @param userinstance.config?: {@link instance.config}\n*/\nexport async function load(instance) {\n if (instance.config.async) { // load models concurrently\n [\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.face,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.emotion,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.handpose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.posenet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.blazepose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.efficientpose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.movenet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.nanodet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.centernet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.faceres,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.segmentation,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n // instance.models.agegenderrace,\n ] = await Promise.all([\n instance.models.face || (instance.config.face.enabled ? facemesh.load(instance.config) : null),\n instance.models.emotion || ((instance.config.face.enabled && instance.config.face.emotion.enabled) ? emotion.load(instance.config) : null),\n instance.models.handpose || (instance.config.hand.enabled ? handpose.load(instance.config) : null),\n instance.models.posenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('posenet') ? posenet.load(instance.config) : null),\n instance.models.blazepose || (instance.config.body.enabled && instance.config.body.modelPath.includes('blazepose') ? blazepose.load(instance.config) : null),\n instance.models.efficientpose || (instance.config.body.enabled && instance.config.body.modelPath.includes('efficientpose') ? efficientpose.load(instance.config) : null),\n instance.models.movenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('movenet') ? movenet.load(instance.config) : null),\n instance.models.nanodet || (instance.config.object.enabled && instance.config.object.modelPath.includes('nanodet') ? nanodet.load(instance.config) : null),\n instance.models.centernet || (instance.config.object.enabled && instance.config.object.modelPath.includes('centernet') ? centernet.load(instance.config) : null),\n instance.models.faceres || ((instance.config.face.enabled && instance.config.face.description.enabled) ? faceres.load(instance.config) : null),\n instance.models.segmentation || (instance.config.segmentation.enabled ? segmentation.load(instance.config) : null),\n // instance.models.agegenderrace || ((instance.config.face.enabled && instance.config.face.agegenderrace.enabled) ? agegenderrace.load(instance.config) : null),\n ]);\n } else { // load models sequentially\n if (instance.config.face.enabled && !instance.models.face) instance.models.face = await facemesh.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.emotion.enabled && !instance.models.emotion) instance.models.emotion = await emotion.load(instance.config);\n if (instance.config.hand.enabled && !instance.models.handpose) instance.models.handpose = await handpose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath.includes('posenet')) instance.models.posenet = await posenet.load(instance.config);\n if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath.includes('blazepose')) instance.models.blazepose = await blazepose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath.includes('efficientpose')) instance.models.efficientpose = await blazepose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath.includes('movenet')) instance.models.movenet = await movenet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath.includes('nanodet')) instance.models.nanodet = await nanodet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath.includes('centernet')) instance.models.centernet = await centernet.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.description.enabled && !instance.models.faceres) instance.models.faceres = await faceres.load(instance.config);\n if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = await segmentation.load(instance.config);\n // if (instance.config.face.enabled && instance.config.face.agegenderrace.enabled && !instance.models.agegenderrace) instance.models.agegenderrace = await agegenderrace.load(instance.config);\n }\n}\n", "/**\n * Module that analyzes person age\n * Obsolete\n */\n\nimport { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as facemesh from './blazeface/facemesh';\nimport * as emotion from './emotion/emotion';\nimport * as faceres from './faceres/faceres';\nimport { Face } from './result';\nimport { Tensor } from './tfjs/types';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nconst rad2deg = (theta) => Math.round((theta * 180) / Math.PI);\n\nconst calculateGaze = (face): { bearing: number, strength: number } => {\n const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = face.mesh[33][2] > face.mesh[263][2]; // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n\n const eyeDiff = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] ** 2) + (eyeDiff[1] ** 2)); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n\n return { bearing, strength };\n};\n\nconst calculateFaceAngle = (face, imageSize): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v) => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a, b) => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a, b) => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r) => {\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n let thetaX; let thetaY; let thetaZ;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n // simple Euler angle calculation based existing 3D mesh\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const meshToEulerAngle = (mesh) => {\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const angle = {\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n // pitch is face move up/down\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n // yaw is face turn left/right\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n // roll is face lean left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n };\n return angle;\n };\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n // make the xyz coordinates proportional, independent of the image/box size\n pt[0] * imageSize[0] / size,\n pt[1] * imageSize[1] / size,\n pt[2],\n ]);\n\n const y_axis = normalize(subVectors(pts[1], pts[0]));\n let x_axis = normalize(subVectors(pts[3], pts[2]));\n const z_axis = normalize(crossVectors(x_axis, y_axis));\n // adjust x_axis to make sure that all axes are perpendicular to each other\n x_axis = crossVectors(y_axis, z_axis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n x_axis[0], x_axis[1], x_axis[2],\n y_axis[0], y_axis[1], y_axis[2],\n z_axis[0], z_axis[1], z_axis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n\nexport const detectFace = async (parent /* instance of human */, input: Tensor): Promise => {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let gearRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n let descRes;\n const faceRes: Array = [];\n parent.state = 'run:face';\n timeStamp = now();\n const faces = await facemesh.predict(input, parent.config);\n parent.performance.face = Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n parent.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefined\n if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);\n\n // run emotion, inherits face from blazeface\n parent.analyze('Start Emotion:');\n if (parent.config.async) {\n emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n } else {\n parent.state = 'run:emotion';\n timeStamp = now();\n emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n parent.performance.emotion = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End Emotion:');\n\n // run gear, inherits face from blazeface\n /*\n parent.analyze('Start GEAR:');\n if (parent.config.async) {\n gearRes = parent.config.face.agegenderrace.enabled ? agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n } else {\n parent.state = 'run:gear';\n timeStamp = now();\n gearRes = parent.config.face.agegenderrace.enabled ? await agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n parent.performance.emotion = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End GEAR:');\n */\n\n // run emotion, inherits face from blazeface\n parent.analyze('Start Description:');\n if (parent.config.async) {\n descRes = parent.config.face.description.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];\n } else {\n parent.state = 'run:description';\n timeStamp = now();\n descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];\n parent.performance.embedding = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End Description:');\n\n // if async wait for results\n if (parent.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes]);\n }\n\n parent.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {\n delete faces[i].annotations.leftEyeIris;\n delete faces[i].annotations.rightEyeIris;\n }\n const irisSize = (faces[i].annotations?.leftEyeIris && faces[i].annotations?.rightEyeIris)\n /* note: average human iris size is 11.7mm */\n ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n : 0;\n\n // optionally return tensor\n const tensor = parent.config.face.detector.return ? tf.squeeze(faces[i].tensor) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n faceRes.push({\n ...faces[i],\n id: i,\n age: descRes.age,\n gender: descRes.gender,\n genderScore: descRes.genderScore,\n embedding: descRes.descriptor,\n emotion: emotionRes,\n iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,\n rotation,\n tensor,\n });\n\n parent.analyze('End Face');\n }\n parent.analyze('End FaceMesh:');\n if (parent.config.async) {\n if (parent.performance.face) delete parent.performance.face;\n if (parent.performance.age) delete parent.performance.age;\n if (parent.performance.gender) delete parent.performance.gender;\n if (parent.performance.emotion) delete parent.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * Gesture detection module\n */\n\nimport { Gesture } from '../result';\n\n/**\n * @typedef FaceGesture\n */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/**\n * @typedef IrisGesture\n */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/**\n * @typedef BodyGesture\n */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/**\n * @typedef BodyGesture\n */\nexport type HandGesture =\n `${'thumb' | 'index finger' | 'middle finger' | 'ring finger' | 'pinky'} forward`\n | `${'thumb' | 'index finger' | 'middle finger' | 'ring finger' | 'pinky'} up`;\n\nexport const body = (res): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\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): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\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[33][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'left' : 'right'}` });\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): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\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 let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > 0.06) gestures.push({ iris: i, gesture: 'looking right' });\n if (rightIrisCenterX > 0.06) gestures.push({ iris: i, gesture: 'looking left' });\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\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 if (finger !== 'palmBase' && Array.isArray(pos)) 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 gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n }\n return gestures;\n};\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep, now } from '../helpers';\nimport type { Result, Face, Body, Hand, Item, Gesture, Person } from '../result';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n */\nexport interface DrawOptions {\n color: string,\n labelColor: string,\n shadowColor: string,\n font: string,\n lineHeight: number,\n lineWidth: number,\n pointSize: number,\n roundRect: number,\n drawPoints: boolean,\n drawLabels: boolean,\n drawBoxes: boolean,\n drawPolygons: boolean,\n drawGaze: boolean,\n fillPolygons: boolean,\n useDepth: boolean,\n useCurves: boolean,\n bufferedOutput: boolean,\n}\n\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n shadowColor: 'black',\n font: 'small-caps 14px \"Segoe UI\"',\n lineHeight: 18,\n lineWidth: 4,\n pointSize: 2,\n roundRect: 8,\n drawPoints: false,\n drawLabels: true,\n drawBoxes: true,\n drawPolygons: true,\n drawGaze: true,\n fillPolygons: false,\n useDepth: true,\n useCurves: false,\n bufferedOutput: true,\n};\n\nconst rad2deg = (theta) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n ctx.beginPath();\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.lineWidth = localOptions.lineWidth;\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nfunction lines(ctx, points: [number, number, number?][] = [], localOptions) {\n if (points === undefined || points.length === 0) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n const z = pt[2] || 0;\n ctx.strokeStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.lineTo(pt[0], Math.round(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nfunction curves(ctx, points: [number, number, number?][] = [], localOptions) {\n if (points === undefined || points.length === 0) return;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n let where: unknown[] = []; // what&where is a record\n let what: unknown[] = []; // what&where is a record\n [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${who}: ${what[1]}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n i += 1;\n }\n }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n for (const f of result) {\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n // silly hack since fillText does not suport new line\n const labels:string[] = [];\n labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n if (f.age) labels.push(`age: ${f.age || ''}`);\n if (f.iris) labels.push(`distance: ${f.iris}`);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n labels.push(emotion.join(' '));\n }\n if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = localOptions.color;\n for (let i = labels.length - 1; i >= 0; i--) {\n const x = Math.max(f.box[0], 0);\n const y = i * localOptions.lineHeight + f.box[1];\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(labels[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(labels[i], x + 4, y + 15);\n }\n ctx.lineWidth = 1;\n if (f.mesh && f.mesh.length > 0) {\n if (localOptions.drawPoints) {\n for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n }\n if (localOptions.drawPolygons) {\n ctx.lineWidth = 1;\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) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n // iris: array[center, left, top, right, bottom]\n if (f.annotations && f.annotations['leftEyeIris']) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations && f.annotations['rightEyeIris']) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = 'pink';\n ctx.beginPath();\n\n const leftGaze = [\n f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n ctx.moveTo(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]);\n ctx.lineTo(leftGaze[0], leftGaze[1]);\n\n const rightGaze = [\n f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n ctx.moveTo(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]);\n ctx.lineTo(rightGaze[0], rightGaze[1]);\n\n ctx.stroke();\n }\n }\n }\n }\n}\n\nexport async function body(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n // @ts-ignore box may not exist\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n // @ts-ignore box may not exist\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n // @ts-ignore box may not exist\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n }\n if (localOptions.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels) {\n ctx.font = localOptions.font;\n if (result[i].keypoints) {\n for (const pt of result[i].keypoints) {\n ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n }\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints) {\n let part;\n const points: [number, number, number?][] = [];\n // shoulder line\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // torso main\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n // leg left\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // leg right\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // arm left\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // arm right\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // draw all\n }\n }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels) {\n const addHandLabel = (part, title) => {\n ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[part.length - 1][2])}, ${127.5 - (2 * part[part.length - 1][2])}, 255, 0.5)` : localOptions.color;\n ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n };\n ctx.font = localOptions.font;\n addHandLabel(h.annotations['indexFinger'], 'index');\n addHandLabel(h.annotations['middleFinger'], 'middle');\n addHandLabel(h.annotations['ringFinger'], 'ring');\n addHandLabel(h.annotations['pinky'], 'pinky');\n addHandLabel(h.annotations['thumb'], 'thumb');\n addHandLabel(h.annotations['palmBase'], 'palm');\n }\n if (localOptions.drawPolygons) {\n const addHandLine = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations['indexFinger']);\n addHandLine(h.annotations['middleFinger']);\n addHandLine(h.annotations['ringFinger']);\n addHandLine(h.annotations['pinky']);\n addHandLine(h.annotations['thumb']);\n // addPart(h.annotations.palmBase);\n }\n }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `${h.label} ${Math.round(100 * h.score)}%`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\nexport async function person(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n if (!inCanvas || !outCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n const outCtx = inCanvas.getContext('2d');\n outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result: Result, drawOptions?: DrawOptions) {\n const timestamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n\n face(inCanvas, result.face, localOptions);\n body(inCanvas, result.body, localOptions);\n hand(inCanvas, result.hand, localOptions);\n object(inCanvas, result.object, localOptions);\n // person(inCanvas, result.persons, localOptions);\n gesture(inCanvas, result.gesture, localOptions); // gestures do not have buffering\n\n /*\n if (!bufferedResult) bufferedResult = result; // first pass\n else if (localOptions.bufferedOutput) calcBuffered(result); // do results interpolation\n else bufferedResult = result; // or just use results as-is\n const promises: Promise[] = [];\n promises.push(face(inCanvas, bufferedResult.face, localOptions));\n promises.push(body(inCanvas, bufferedResult.body, localOptions));\n promises.push(hand(inCanvas, bufferedResult.hand, localOptions));\n promises.push(object(inCanvas, bufferedResult.object, localOptions));\n // promises.push(person(inCanvas, bufferedResult.persons, localOptions));\n promises.push(gesture(inCanvas, result.gesture, localOptions)); // gestures do not have buffering\n // await Promise.all(promises);\n */\n result.performance.draw = Math.trunc(now() - timestamp);\n}\n", "/**\n * Module that analyzes existing results and recombines them into a unified person object\n */\n\nimport { Face, Body, Hand, Gesture, Person } from './result';\n\nexport function join(faces: Array, bodies: Array, hands: Array, gestures: Array, shape: Array | undefined): Array {\n let id = 0;\n const persons: Array = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: Person = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n }\n\n // create new overarching box from all boxes beloning to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face?.box);\n extractXY(person.body?.box);\n extractXY(person.hands?.left?.box);\n extractXY(person.hands?.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape && shape.length === 4) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Module that interpolates results for smoother animations\n */\n\nimport type { Result, Face, Body, Hand, Item, Gesture, Person } from './result';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };\n\nexport function calc(newResult: Result): Result {\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n // curve fitted: buffer = 8 - ln(delay)\n // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n // - at 50ms delay buffer = ~4.1 => 28% towards live data\n // - at 250ms delay buffer = ~2.5 => 40% towards live data\n // - at 500ms delay buffer = ~1.8 => 55% towards live data\n // - at 750ms delay buffer = ~1.4 => 71% towards live data\n // - at 1sec delay buffer = 1 which means live data is used\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed) : 1;\n\n bufferedResult.canvas = newResult.canvas;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as Body[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + b) / bufferedFactor) as [number, number, number, number];\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + b) / bufferedFactor) as [number, number, number, number];\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((keypoint, j) => ({\n score: keypoint.score,\n part: keypoint.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[0] + keypoint.position[0]) / bufferedFactor : keypoint.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[1] + keypoint.position[1]) / bufferedFactor : keypoint.position[1],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[0] + keypoint.positionRaw[0]) / bufferedFactor : keypoint.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[1] + keypoint.positionRaw[1]) / bufferedFactor : keypoint.position[1],\n ],\n }))) as Array<{ score: number, part: string, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as Hand[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n const keypoints = newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * bufferedResult.hand[i].keypoints[j][k] + coord) / bufferedFactor)) as [number, number, number]);\n const keys = Object.keys(newResult.hand[i].annotations); // update annotations\n const annotations = {};\n for (const key of keys) {\n annotations[key] = newResult.hand[i].annotations[key]\n .map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor));\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as Face[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as Item[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as Person[]));\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as [number, number, number, number];\n }\n }\n }\n\n // just copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture as Gesture[];\n if (newResult.performance) bufferedResult.performance = newResult.performance;\n\n return bufferedResult;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Human main module\n */\n\nimport { log, now, mergeDeep } from './helpers';\nimport { Config, defaults } from './config';\nimport { Result, Gesture } from './result';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as models from './models';\nimport * as face from './face';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceres from './faceres/faceres';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as movenet from './movenet/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as centernet from './object/centernet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as persons from './persons';\nimport * as interpolate from './interpolate';\nimport * as sample from './sample';\nimport * as app from '../package.json';\nimport { Tensor, GraphModel } from './tfjs/types';\n\n// export types\nexport type { Config } from './config';\nexport type { Result, Face, Hand, Body, Item, Gesture, Person } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection\n * @typedef Input Type\n */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message\n * @typedef Error Type\n */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS\n * @external\n */\nexport type TensorFlow = typeof tf;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig: {@link Config}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n /** Current configuration\n * - Details: {@link Config}\n */\n config: Config;\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n /** @internal: Instance of current image being processed */\n image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n /** @internal: Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n */\n tf: TensorFlow;\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - options: {@link DrawOptions} global settings for all draw operations, can be overriden for each draw method\n * - face: draw detected faces\n * - body: draw detected people and body parts\n * - hand: draw detected hands and hand parts\n * - canvas: draw processed canvas which is a processed copy of the input\n * - all: meta-function that performs: canvas, face, body, hand\n */\n draw: {\n options: draw.DrawOptions,\n gesture: typeof draw.gesture,\n face: typeof draw.face,\n body: typeof draw.body,\n hand: typeof draw.hand,\n canvas: typeof draw.canvas,\n all: typeof draw.all,\n };\n /** @internal: Currently loaded models */\n models: {\n face: [unknown, GraphModel | null, GraphModel | null] | null,\n posenet: GraphModel | null,\n blazepose: GraphModel | null,\n efficientpose: GraphModel | null,\n movenet: GraphModel | null,\n handpose: [GraphModel | null, GraphModel | null] | null,\n age: GraphModel | null,\n gender: GraphModel | null,\n emotion: GraphModel | null,\n embedding: GraphModel | null,\n nanodet: GraphModel | null,\n centernet: GraphModel | null,\n faceres: GraphModel | null,\n segmentation: GraphModel | null,\n };\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: typeof facemesh.triangulation;\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: typeof facemesh.uvmap;\n /** Platform and agent information detected by Human */\n sysinfo: { platform: string, agent: string };\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n #firstRun: boolean;\n #lastInputSum: number;\n #lastCacheDiff: number;\n\n // definition end\n\n /**\n * Creates instance of Human library that is futher used for all operations\n * @param userConfig: {@link Config}\n */\n constructor(userConfig?: Config | Record) {\n this.config = mergeDeep(defaults, userConfig || {});\n this.tf = tf;\n this.draw = draw;\n this.version = app.version;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.#firstRun = true;\n this.#lastCacheDiff = 0;\n this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };\n // object that contains all initialized models\n this.models = {\n face: null,\n posenet: null,\n blazepose: null,\n efficientpose: null,\n movenet: null,\n handpose: null,\n age: null,\n gender: null,\n emotion: null,\n embedding: null,\n nanodet: null,\n centernet: null,\n faceres: null,\n segmentation: null,\n };\n // export access to image processing\n // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n this.image = (input: Input) => image.process(input, this.config);\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // include platform info\n this.sysinfo = sysinfo.info();\n this.#lastInputSum = 1;\n }\n\n // helper function: measure tensor leak\n /** @hidden */\n analyze = (...msg) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n /** @hidden */\n #sanity = (input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n * - Calculation is based on normalized Minkowski distance between\n *\n * @param embedding1: face descriptor as array of numbers\n * @param embedding2: face descriptor as array of numbers\n * @returns similarity: number\n */\n // eslint-disable-next-line class-methods-use-this\n similarity(embedding1: Array, embedding2: Array): number {\n return faceres.similarity(embedding1, embedding2);\n }\n\n /**\n * Segmentation method takes any input and returns processed canvas with body segmentation\n * Optional parameter background is used to fill the background with specific input\n * Segmentation is not triggered as part of detect process\n *\n * @param input: {@link Input}\n * @param background?: {@link Input}\n * @returns Canvas\n */\n segmentation(input: Input, background?: Input) {\n return segmentation.process(input, background, this.config);\n }\n\n /** Enhance method performs additional enhacements to face image previously detected for futher processing\n * @param input: Tensor as provided in human.result.face[n].tensor\n * @returns Tensor\n */\n // eslint-disable-next-line class-methods-use-this\n enhance(input: Tensor): Tensor | null {\n // @ts-ignore type mismach for Tensor\n return faceres.enhance(input);\n }\n\n /** Math method find best match between provided face descriptor and predefined database of known descriptors\n * @param faceEmbedding: face descriptor previsouly calculated on any face\n * @param db: array of mapping of face descriptors to known values\n * @param threshold: minimum score for matching to be considered in the result\n * @returns best match\n */\n // eslint-disable-next-line class-methods-use-this\n match(faceEmbedding: Array, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n return faceres.match(faceEmbedding, db, threshold);\n }\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n * @param userConfig?: {@link Config}\n */\n async load(userConfig?: Config | Record) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.#firstRun) { // print version info on first run and check for correct backend setup\n if (this.config.debug) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n if (this.config.debug) log('platform:', this.sysinfo.platform);\n if (this.config.debug) log('agent:', this.sysinfo.agent);\n\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\n await models.load(this); // actually loads models\n\n if (this.#firstRun) { // print memory stats on first run\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.performance.load as number || 0)) this.performance.load = current;\n }\n\n // check if backend needs initialization if it changed\n /** @hidden */\n #checkBackend = async (force = false) => {\n if (this.config.backend && (this.config.backend.length > 0) && 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.length > 0) {\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && this.config.debug) log('running inside web worker');\n\n // force browser vs node backend\n if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('wasm path:', this.config.wasmPath);\n if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n else throw new Error('Human: WASM backend is not loaded');\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n // this.tf.enableDebugMode();\n if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n this.tf.ENV.set('WEBGL_CPU_FORWARD', true);\n this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n this.tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n // if (!this.config.object.enabled) this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (typeof this.config['deallocate'] !== 'undefined' && this.config['deallocate']) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n // @ts-ignore getGPGPUContext only exists on WebGL backend\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.performance.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n /**\n * Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result?: {@link Result} optional use specific result set to run interpolation on\n * @returns result: {@link Result}\n */\n next = (result?: Result) => interpolate.calc(result || this.result) as Result;\n\n // check if input changed sufficiently to trigger new detections\n /** @hidden */\n #skipFrame = async (input) => {\n if (this.config.cacheSensitivity === 0) return false;\n const resizeFact = 32;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc(input.shape[1] / resizeFact), Math.trunc(input.shape[2] / resizeFact)]);\n // use tensor sum\n /*\n const sumT = this.tf.sum(reduced);\n const sum = sumT.dataSync()[0] as number;\n sumT.dispose();\n */\n // use js loop sum, faster than uploading tensor to gpu calculating and downloading back\n const reducedData = await reduced.data(); // raw image rgb array\n let sum = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum += reducedData[3 * i + 2]; // look only at green value of each pixel\n\n reduced.dispose();\n const diff = 100 * (Math.max(sum, this.#lastInputSum) / Math.min(sum, this.#lastInputSum) - 1);\n this.#lastInputSum = sum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n const skipFrame = diff < Math.max(this.config.cacheSensitivity, this.#lastCacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n this.#lastCacheDiff = diff > 10 * this.config.cacheSensitivity ? 0 : diff;\n return skipFrame;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input: Input\n * @param userConfig?: {@link Config}\n * @returns result: {@link Result}\n */\n async detect(input: Input, userConfig?: Config | Record): Promise {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n let elapsedTime;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\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 const timeStart = now();\n\n // configure backend\n await this.#checkBackend();\n\n // load models if enabled\n await this.load();\n\n /*\n // function disabled in favor of inputChanged\n // disable video optimization for inputs of type image, but skip if inside worker thread\n let previousVideoOptimized;\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (input && this.config.videoOptimized && (typeof window !== 'undefined') && (typeof WorkerGlobalScope !== 'undefined') && (\n (typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n || (typeof Image !== 'undefined' && input instanceof Image)\n || (typeof ImageData !== 'undefined' && input instanceof ImageData)\n || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap))\n ) {\n log('disabling video optimization');\n previousVideoOptimized = this.config.videoOptimized;\n this.config.videoOptimized = false;\n }\n */\n\n timeStamp = now();\n let process = image.process(input, this.config);\n this.performance.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run segmentation preprocessing\n if (this.config.segmentation.enabled && process && process.tensor) {\n this.analyze('Start Segmentation:');\n this.state = 'run:segmentation';\n timeStamp = now();\n await segmentation.predict(process);\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.segmentation = elapsedTime;\n if (process.canvas) {\n // replace input\n tf.dispose(process.tensor);\n process = image.process(process.canvas, this.config);\n }\n this.analyze('End Segmentation:');\n }\n\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\n timeStamp = now();\n this.config.skipFrame = await this.#skipFrame(process.tensor);\n if (!this.performance.frames) this.performance.frames = 0;\n if (!this.performance.cached) this.performance.cached = 0;\n (this.performance.frames as number)++;\n if (this.config.skipFrame) this.performance.cached++;\n this.performance.changed = Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes;\n let bodyRes;\n let handRes;\n let objectRes;\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 ? face.detectFace(this, process.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, process.tensor) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.face = elapsedTime;\n }\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(process.tensor, this.config) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.body = elapsedTime;\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 ? handpose.predict(process.tensor, this.config) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await handpose.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.hand = elapsedTime;\n }\n this.analyze('End Hand:');\n\n // run nanodet\n this.analyze('Start Object:');\n if (this.config.async) {\n if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(process.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n this.state = 'run:object';\n timeStamp = now();\n if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.object = elapsedTime;\n }\n this.analyze('End Object:');\n\n // if async wait for results\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n let gestureRes: Gesture[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.performance.gesture = Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n this.result = {\n face: faceRes,\n body: bodyRes,\n hand: handRes,\n gesture: gestureRes,\n object: objectRes,\n performance: this.performance,\n canvas: process.canvas,\n timestamp: Date.now(),\n get persons() { return persons.join(faceRes, bodyRes, handRes, gestureRes, process?.tensor?.shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(process.tensor);\n\n // log('Result:', result);\n resolve(this.result);\n });\n }\n\n /** @hidden */\n #warmupBitmap = async () => {\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 /** @hidden */\n #warmupCanvas = async () => 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-upper.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 /** @hidden */\n #warmupNode = async () => {\n const atob = (str) => Buffer.from(str, 'base64');\n let img;\n if (this.config.warmup === 'face') img = atob(sample.face);\n if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n if (!img) return null;\n let res;\n if (typeof tf['node'] !== 'undefined') {\n const data = tf['node'].decodeJpeg(img);\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n } else {\n if (this.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await this.detect(input, this.config);\n */\n }\n return res;\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n */\n async warmup(userConfig?: Config | Record): Promise {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\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 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\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"], - "mappings": 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+ "sourcesContent": ["/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${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\n// helper function: gets elapsed time on both browser and nodejs\nexport const 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\nexport function 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\n// helper function: return min and max from input array\nexport const minmax = (data) => data.reduce((acc, val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n * @typedef Config\n */\nexport interface Config {\n /** Backend used for TFJS operations */\n backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n\n /** Path to *.wasm files if backend is set to `wasm` */\n wasmPath: string,\n\n /** Print debug statements to console */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n */\n warmup: 'none' | 'face' | 'full' | 'body',\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n */\n modelBasePath: string,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n */\n cacheSensitivity: number;\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n */\n skipFrame: boolean;\n\n /** Run input through image filters before inference\n * - image filters run with near-zero latency as they are executed on the GPU\n */\n filter: {\n enabled: boolean,\n /** Resize input width\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 */\n width: number,\n /** 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 */\n height: number,\n /** Return processed canvas imagedata in result */\n return: boolean,\n /** Flip input as mirror image */\n flip: boolean,\n /** Range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** Range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** Image negative */\n negative: boolean,\n /** Image sepia colors */\n sepia: boolean,\n /** Image vintage colors */\n vintage: boolean,\n /** Image kodachrome colors */\n kodachrome: boolean,\n /** Image technicolor colors */\n technicolor: boolean,\n /** Image polaroid camera effect */\n polaroid: boolean,\n /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n },\n // type definition end\n\n /** Controlls gesture detection */\n gesture: {\n enabled: boolean,\n },\n\n /** Controlls and configures all face-specific options:\n * - face detection, face mesh detection, age, gender, emotion detection and face description\n * Parameters:\n * - enabled: true/false\n * - modelPath: path for each of face models\n * - minConfidence: threshold for discarding a prediction\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of faces detected in the input, should be set to the minimum number for performance\n * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n * - return: return extracted face as tensor for futher user processing, in which case user is reponsible for manually disposing the tensor\n */\n face: {\n enabled: boolean,\n detector: {\n modelPath: string,\n rotation: boolean,\n maxDetected: number,\n skipFrames: number,\n minConfidence: number,\n iouThreshold: number,\n return: boolean,\n },\n mesh: {\n enabled: boolean,\n modelPath: string,\n },\n iris: {\n enabled: boolean,\n modelPath: string,\n },\n description: {\n enabled: boolean,\n modelPath: string,\n skipFrames: number,\n minConfidence: number,\n },\n emotion: {\n enabled: boolean,\n minConfidence: number,\n skipFrames: number,\n modelPath: string,\n },\n },\n\n /** Controlls and configures all body detection specific options\n * - enabled: true/false\n * - modelPath: body pose model, can be absolute path or relative to modelBasePath\n * - minConfidence: threshold for discarding a prediction\n * - maxDetected: maximum number of people detected in the input, should be set to the minimum number for performance\n */\n body: {\n enabled: boolean,\n modelPath: string,\n maxDetected: number,\n minConfidence: number,\n skipFrames: number,\n },\n\n /** Controlls and configures all hand detection specific options\n * - enabled: true/false\n * - landmarks: detect hand landmarks or just hand boundary box\n * - modelPath: paths for hand detector and hand skeleton models, can be absolute path or relative to modelBasePath\n * - minConfidence: threshold for discarding a prediction\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of hands detected in the input, should be set to the minimum number for performance\n * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n */\n hand: {\n enabled: boolean,\n rotation: boolean,\n skipFrames: number,\n minConfidence: number,\n iouThreshold: number,\n maxDetected: number,\n landmarks: boolean,\n detector: {\n modelPath: string,\n },\n skeleton: {\n modelPath: string,\n },\n },\n\n /** Controlls and configures all object detection specific options\n * - enabled: true/false\n * - modelPath: object detection model, can be absolute path or relative to modelBasePath\n * - minConfidence: minimum score that detection must have to return as valid object\n * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n * - maxDetected: maximum number of detections to return\n */\n object: {\n enabled: boolean,\n modelPath: string,\n minConfidence: number,\n iouThreshold: number,\n maxDetected: number,\n skipFrames: number,\n },\n\n /** Controlls and configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n *\n * - enabled: true/false\n * - modelPath: object detection model, can be absolute path or relative to modelBasePath\n */\n segmentation: {\n enabled: boolean,\n modelPath: string,\n },\n}\n\nconst config: Config = {\n backend: 'webgl', // select tfjs backend to use, leave empty to use default backend\n // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n modelBasePath: '../models/', // base path for all models\n wasmPath: '../node_modules/@tensorflow/tfjs-backend-wasm/dist/', // path for wasm binaries, only used for backend: wasm\n debug: true, // print additional status messages to console\n async: true, // execute enabled models in parallel\n warmup: 'full', // 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 // only used for `webgl` and `humangl` backends\n cacheSensitivity: 0.75, // cache sensitivity\n // values 0..1 where 0.01 means reset cache if input changed more than 1%\n // set to 0 to disable caching\n skipFrame: false, // internal & dynamic\n filter: { // run input through image filters before inference\n // image filters run with near-zero latency as they are executed on the GPU\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 flip: false, // flip input as mirror image\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 gesture recognition based on model results\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: 'blazeface.json', // detector model, can be absolute path or relative to modelBasePath\n rotation: true, // 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 // this parameter is not valid in nodejs\n maxDetected: 15, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 15, // how many max frames to go without re-running the face bounding box detector\n // only used when cacheSensitivity is not zero\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 minConfidence: 0.2, // threshold for discarding a prediction\n iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed\n return: false, // return extracted face as tensor\n // in which case user is reponsible for disposing the tensor\n },\n\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json', // facemesh model, can be absolute path or relative to modelBasePath\n },\n\n iris: {\n enabled: true,\n modelPath: 'iris.json', // face iris model\n // can be either absolute path or relative to modelBasePath\n },\n\n description: {\n enabled: true, // to improve accuracy of face description extraction it is\n // recommended to enable detector.rotation and mesh.enabled\n modelPath: 'faceres.json', // face description model\n // can be either absolute path or relative to modelBasePath\n skipFrames: 11, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n minConfidence: 0.1, // threshold for discarding a prediction\n },\n\n emotion: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n skipFrames: 17, // how max many frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n modelPath: 'emotion.json', // face emotion model, can be absolute path or relative to modelBasePath\n },\n },\n\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json', // body model, can be absolute path or relative to modelBasePath\n // can be 'posenet', 'blazepose', 'efficientpose', 'movenet-lightning', 'movenet-thunder'\n maxDetected: 1, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n // only valid for posenet as other models detects single pose\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 1, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n},\n\n hand: {\n enabled: true,\n rotation: true, // 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 skipFrames: 18, // how many max frames to go without re-running the hand bounding box detector\n // only used when cacheSensitivity is not zero\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 minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed\n maxDetected: 2, // 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: 'handdetect.json', // hand detector model, can be absolute path or relative to modelBasePath\n },\n skeleton: {\n modelPath: 'handskeleton.json', // hand skeleton model, can be absolute path or relative to modelBasePath\n },\n },\n\n object: {\n enabled: false,\n modelPath: 'mb3-centernet.json', // experimental: object detection model, can be absolute path or relative to modelBasePath\n // can be 'mb3-centernet' or 'nanodet'\n minConfidence: 0.2, // threshold for discarding a prediction\n iouThreshold: 0.4, // ammount of overlap between two detected objects before one object is removed\n maxDetected: 10, // maximum number of objects detected in the input\n skipFrames: 19, // how many max frames to go without re-running the detector\n // only used when cacheSensitivity is not zero\n },\n\n segmentation: {\n enabled: false, // controlls and configures all body segmentation module\n // removes background from input containing person\n // if segmentation is enabled it will run as preprocessing task before any other model\n // alternatively leave it disabled and use it on-demand using human.segmentation method which can\n // remove background or replace it with user-provided background\n modelPath: 'selfie.json', // experimental: object detection model, can be absolute path or relative to modelBasePath\n // can be 'selfie' or 'meet'\n },\n};\nexport { config as defaults };\n", "/**\n * Helper function that returns basic system info\n */\nexport function info(): { platform: string, agent: string } {\n let platform;\n let agent;\n if (typeof navigator !== 'undefined') {\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw && raw[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n agent = navigator.userAgent.replace(raw[0], '');\n if (platform[1]) agent = agent.replace(raw[1], '');\n agent = agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n platform = `${process.platform} ${process.arch}`;\n agent = `NodeJS ${process.version}`;\n }\n return { platform, agent };\n}\n", "/**\n * Creates tfjs bundle used by Human browser build target\n * @external\n */\n\n// get versions of all packages\nimport { version as tfjsVersion } from '@tensorflow/tfjs/package.json';\nimport { version as tfjsCoreVersion } from '@tensorflow/tfjs-core/package.json';\nimport { version as tfjsDataVersion } from '@tensorflow/tfjs-data/package.json';\nimport { version as tfjsLayersVersion } from '@tensorflow/tfjs-layers/package.json';\nimport { version as tfjsConverterVersion } from '@tensorflow/tfjs-converter/package.json';\nimport { version as tfjsBackendCPUVersion } from '@tensorflow/tfjs-backend-cpu/package.json';\nimport { version as tfjsBackendWebGLVersion } from '@tensorflow/tfjs-backend-webgl/package.json';\nimport { version as tfjsBackendWASMVersion } from '@tensorflow/tfjs-backend-wasm/package.json';\n\n// export all from sources\n// requires treeShaking:ignore-annotations due to tfjs misconfiguration\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export all from build\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// export * from '@tensorflow/tfjs-backend-webgpu/dist/index.js'; // experimental\n\n// export versions\nexport const version = {\n tfjs: tfjsVersion,\n 'tfjs-core': tfjsCoreVersion,\n 'tfjs-data': tfjsDataVersion,\n 'tfjs-layers': tfjsLayersVersion,\n 'tfjs-converter': tfjsConverterVersion,\n 'tfjs-backend-cpu': tfjsBackendCPUVersion,\n 'tfjs-backend-webgl': tfjsBackendWebGLVersion,\n 'tfjs-backend-wasm': tfjsBackendWASMVersion,\n};\n", "/**\n * Custom TFJS backend for Human based on WebGL\n * Not used by default\n */\n\nimport { log } from '../helpers';\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 extensions: [],\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\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions() as string[];\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(): void {\n if (!tf.findBackend(config.name)) {\n // log('backend registration:', config.name);\n try {\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 config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\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 } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n log('backend registered:', config.name);\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,\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 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, 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 = [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)];\n const endPoint = [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function 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\nexport const disposeBox = (t) => {\n tf.dispose(t.startPoint);\n tf.dispose(t.endPoint);\n};\n\nexport const createBox = (startEndTensor) => ({\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\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\nexport function 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", "import { log, join, mergeDeep } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\nimport { Config } from '../config';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nconst keypointsCount = 6;\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 model: GraphModel;\n anchorsData: [number, number][];\n anchors: Tensor;\n inputSize: number;\n config: Config;\n\n constructor(model, config: Config) {\n this.model = model;\n this.anchorsData = util.generateAnchors(model.inputs[0].shape[1]);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = model.inputs[0].shape[2];\n this.config = config;\n }\n\n async getBoundingBoxes(inputImage: Tensor, userConfig: Config) {\n // sanity check on input\n // @ts-ignore isDisposed is internal property\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [batch, boxes, scores] = tf.tidy(() => {\n const resizedImage = tf.image.resizeBilinear(inputImage, [this.inputSize, this.inputSize]);\n const normalizedImage = tf.sub(tf.div(resizedImage, 127.5), 0.5);\n const res = this.model.execute(normalizedImage);\n let batchOut;\n if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n const sorted = res.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 batchOut = tf.squeeze(concat, 0);\n } else {\n batchOut = tf.squeeze(res); // when using tfhub model\n }\n const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n const scoresOut = tf.squeeze(tf.sigmoid(logits)); // inside tf.tidy\n return [batchOut, boxesOut, scoresOut];\n });\n\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n const nmsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);\n const nms = await nmsTensor.array();\n tf.dispose(nmsTensor);\n const annotatedBoxes: Array<{ box: { startPoint: Tensor, endPoint: Tensor }, landmarks: Tensor, anchor: number[], confidence: number }> = [];\n const scoresData = await scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scoresData[nms[i]];\n if (confidence > this.config.face.detector.minConfidence) {\n const boundingBox = tf.slice(boxes, [nms[i], 0], [1, -1]);\n const localBox = box.createBox(boundingBox);\n tf.dispose(boundingBox);\n const anchor = this.anchorsData[nms[i]];\n const landmarks = tf.tidy(() => tf.reshape(tf.squeeze(tf.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));\n annotatedBoxes.push({ box: localBox, landmarks, anchor, confidence });\n }\n }\n tf.dispose(batch);\n tf.dispose(boxes);\n tf.dispose(scores);\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n };\n }\n}\n\nexport async function load(config: Config) {\n const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const blazeFace = new BlazeFaceModel(model, config);\n if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n return blazeFace;\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", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { BlazeFaceModel } from './blazeface';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 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: Array<{ startPoint: number[], endPoint: number[], landmarks: Array, confidence: number, faceConfidence?: number }>;\n boundingBoxDetector: BlazeFaceModel; // tf.GraphModel\n meshDetector: GraphModel; // tf.GraphModel\n irisModel: GraphModel; // tf.GraphModel\n boxSize: number;\n meshSize: number;\n irisSize: number;\n irisEnlarge: number;\n skipped: number;\n detectedFaces: number;\n\n constructor(boundingBoxDetector, meshDetector, irisModel) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n this.irisSize = irisModel?.inputs[0].shape[1] || 0;\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 coordsScaled = rawCoords.map((coord) => ([\n boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n 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 Math.round(coord[0] + util.dot(boxCenter, inverseRotationMatrix[0])),\n Math.round(coord[1] + util.dot(boxCenter, inverseRotationMatrix[1])),\n Math.round(coord[2]),\n ]));\n }\n\n // eslint-disable-next-line class-methods-use-this\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[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(bounding.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.meshSize,\n box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n box.endPoint[0] / this.meshSize,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip && tf.ENV.flags.IS_BROWSER) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\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<[number, number, number]> = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; 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(irisLandmarks.index) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n // eslint-disable-next-line class-methods-use-this\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][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 correctFaceRotation(config, box, input) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n const 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); // rotateWithOffset is not defined for tfjs-node\n const rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n const cut = config.face.mesh.enabled\n ? bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize])\n : bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]);\n const face = tf.div(cut, 255);\n tf.dispose(cut);\n tf.dispose(rotatedImage);\n return [angle, rotationMatrix, face];\n }\n\n async augmentIris(rawCoords, face) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = this.irisModel.predict(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data(); // inside tf.tidy\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 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', null);\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\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\n // 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 replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\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 const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\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.skipFrame) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input, config);\n this.skipped = 0;\n }\n if (config.skipFrame) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (!config.skipFrame || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxDetected)))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n const startPoint = await possible.box.startPoint.data();\n const endPoint = await possible.box.endPoint.data();\n const landmarks = await possible.landmarks.array();\n this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\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;\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 tf.dispose(prediction.box.startPoint);\n tf.dispose(prediction.box.endPoint);\n tf.dispose(prediction.landmarks);\n });\n }\n\n const results: Array<{ mesh, box, faceConfidence, boxConfidence, confidence, image }> = [];\n for (let i = 0; i < this.storedBoxes.length; i++) {\n let box = this.storedBoxes[i]; // 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\n if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n [angle, rotationMatrix, face] = this.correctFaceRotation(config, box, input);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const clonedImage = input.clone();\n const cut = config.face.mesh.enabled\n ? bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize])\n : bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]);\n face = tf.div(cut, 255);\n tf.dispose(cut);\n tf.dispose(clonedImage);\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 results.push({\n mesh: [],\n box,\n faceConfidence: null,\n boxConfidence: box.confidence,\n confidence: box.confidence,\n image: face,\n });\n } else {\n const [contours, confidence, contourCoords] = this.meshDetector.execute(face) as Array; // The first returned tensor represents facial contours which are already included in the coordinates.\n tf.dispose(contours);\n const faceConfidence = (await confidence.data())[0] as number; // inside tf.tidy\n tf.dispose(confidence);\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(contourCoords);\n tf.dispose(coordsReshaped);\n if (faceConfidence < config.face.detector.minConfidence) {\n this.storedBoxes[i].confidence = faceConfidence; // reset confidence of cached box\n tf.dispose(face);\n } else {\n if (config.face.iris.enabled) rawCoords = await this.augmentIris(rawCoords, face);\n\n // override box from detection with one calculated from mesh\n const mesh = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const storeConfidence = box.confidence;\n // @ts-ignore enlargeBox does not include confidence so we append it manually\n box = bounding.enlargeBox(bounding.calculateLandmarksBoundingBox(mesh), 1.5); // redefine box with mesh calculated one\n box.confidence = storeConfidence;\n\n // do rotation one more time with mesh keypoints if we want to return perfect image\n if (config.face.detector.rotation && config.face.mesh.enabled && config.face.description.enabled && tf.ENV.flags.IS_BROWSER) {\n [angle, rotationMatrix, face] = this.correctFaceRotation(config, box, input);\n }\n\n results.push({\n mesh,\n box,\n faceConfidence,\n boxConfidence: box.confidence,\n confidence: faceConfidence,\n image: face,\n });\n\n // updated stored cache values\n this.storedBoxes[i] = { ...bounding.squarifyBox(box), confidence: box.confidence, faceConfidence };\n }\n }\n }\n\n // results = results.filter((a) => a !== null);\n // remove cache entries for detected boxes on low confidence\n if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.confidence > config.face.detector.minConfidence);\n this.detectedFaces = results.length;\n\n return results;\n }\n}\n", "/**\n * FaceMesh & BlazeFace Module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Face } from '../result';\nimport { Config } from '../config';\n\nlet faceModels: [blazeface.BlazeFaceModel | null, GraphModel | null, GraphModel | null] = [null, null, null];\nlet facePipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await facePipeline.predict(input, config);\n const results: Array = [];\n let id = 0;\n for (const prediction of (predictions || [])) {\n if (!prediction || prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const meshRaw = prediction.mesh.map((pt) => [\n pt[0] / (input.shape[2] || 0),\n pt[1] / (input.shape[1] || 0),\n pt[2] / facePipeline.meshSize,\n ]);\n const annotations = {};\n if (prediction.mesh && prediction.mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => prediction.mesh[index]);\n }\n const clampedBox: [number, number, number, number] = prediction.box ? [\n Math.trunc(Math.max(0, prediction.box.startPoint[0])),\n Math.trunc(Math.max(0, prediction.box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])),\n ] : [0, 0, 0, 0];\n const boxRaw: [number, number, number, number] = prediction.box ? [\n prediction.box.startPoint[0] / (input.shape[2] || 0),\n prediction.box.startPoint[1] / (input.shape[1] || 0),\n (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),\n (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0),\n ] : [0, 0, 0, 0];\n results.push({\n id: id++,\n score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n boxScore: Math.round(100 * prediction.boxConfidence) / 100,\n faceScore: Math.round(100 * prediction.faceConfidence) / 100,\n box: clampedBox,\n boxRaw,\n mesh: prediction.mesh,\n meshRaw,\n annotations,\n tensor: prediction.image,\n });\n if (prediction.coords) tf.dispose(prediction.coords);\n }\n return results;\n}\n\nexport async function load(config): Promise<[unknown, GraphModel | null, GraphModel | null]> {\n if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n // @ts-ignore type mismatch for GraphModel\n faceModels = await Promise.all([\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n if (config.face.mesh.enabled) {\n if (!faceModels[1] || !faceModels[1]['modelUrl']) log('load model failed:', config.face.mesh.modelPath);\n else if (config.debug) log('load model:', faceModels[1]['modelUrl']);\n }\n if (config.face.iris.enabled) {\n if (!faceModels[2] || !faceModels[2]['modelUrl']) log('load model failed:', config.face.iris.modelPath);\n else if (config.debug) log('load model:', faceModels[2]['modelUrl']);\n }\n } else if (config.debug) {\n if (faceModels[0]) log('cached model:', faceModels[0].model['modelUrl']);\n if (faceModels[1]) log('cached model:', faceModels[1]['modelUrl']);\n if (faceModels[2]) log('cached model:', faceModels[2]['modelUrl']);\n }\n facePipeline = new facepipeline.Pipeline(faceModels[0], faceModels[1], faceModels[2]);\n return faceModels;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * HSE-FaceRes Module\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\nconst last: Array<{\n age: number,\n gender: string,\n genderScore: number,\n descriptor: number[],\n}> = [];\n\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config: Config): Promise {\n const modelUrl = join(config.modelBasePath, config.face.description.modelPath);\n if (!model) {\n // @ts-ignore type mismatch for GraphModel\n model = await tf.loadGraphModel(modelUrl);\n if (!model) log('load model failed:', config.face.description.modelPath);\n else if (config.debug) log('load model:', modelUrl);\n } else if (config.debug) log('cached model:', modelUrl);\n return model;\n}\n\nexport function similarity(embedding1: Array, embedding2: Array, order = 2): number {\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, euclidean distance is limited case where order is 2\n const distance = 5.0 * embedding1\n .map((_val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n .reduce((sum, now) => (sum + now), 0) // sum all distances\n ** (1 / order); // get root of\n const res = Math.max(0, 100 - distance) / 100.0;\n return res;\n}\n\nexport function match(embedding: Array, db: DB, threshold = 0) {\n let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n for (const f of db) {\n if (f.embedding && f.name) {\n const perc = similarity(embedding, f.embedding);\n if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n }\n }\n return best;\n}\n\nexport function enhance(input): Tensor {\n const image = tf.tidy(() => {\n // input received from detector is already normalized to 0..1\n // input is also assumed to be straightened\n const tensor = input.image || input.tensor || input;\n if (!(tensor instanceof tf.Tensor)) return null;\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n if (!model.inputs[0].shape) return null; // model has no shape so no point continuing\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n /*\n // just resize to fit the embedding model instead of cropping\n const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n */\n\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 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 const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n\n /*\n // increase image pseudo-contrast 100%\n // (or do it per-channel so mean is done on each channel)\n // (or calculate histogram and do it based on histogram)\n const mean = merge.mean();\n const factor = 2;\n const contrast = merge.sub(mean).mul(factor).add(mean);\n */\n\n /*\n // normalize brightness from 0..1\n // silly way of creating pseudo-hdr of image\n const darken = crop.sub(crop.min());\n const lighten = darken.div(darken.max());\n */\n\n const norm = tf.mul(crop, 255);\n\n return norm;\n });\n return image;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count) {\n if (!model) return null;\n if ((skipped < config.face.description.skipFrames) && config.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const enhanced = enhance(image);\n\n let resT;\n const obj = {\n age: 0,\n gender: 'unknown',\n genderScore: 0,\n descriptor: [],\n };\n\n if (config.face.description.enabled) resT = await model.predict(enhanced);\n tf.dispose(enhanced);\n\n if (resT) {\n const gender = await resT.find((t) => t.shape[1] === 1).data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > config.face.description.minConfidence) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n const age = (await argmax.data())[0];\n const all = await resT.find((t) => t.shape[1] === 100).data(); // inside tf.tidy\n obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n\n const descriptor = await desc.data();\n obj.descriptor = [...descriptor];\n resT.forEach((t) => tf.dispose(t));\n }\n\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * Emotion Module\n */\n\nimport { log, join } from '../helpers';\nimport { Config } from '../config';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\n// let last: Array<{ score: number, emotion: string }> = [];\nconst last: Array> = [];\nlet lastCount = 0;\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\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count) {\n if (!model) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n tf.dispose(resize);\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 tf.dispose(red);\n tf.dispose(green);\n tf.dispose(blue);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n tf.dispose(redNorm);\n tf.dispose(greenNorm);\n tf.dispose(blueNorm);\n const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));\n tf.dispose(grayscale);\n const obj: Array<{ score: number, emotion: string }> = [];\n if (config.face.emotion.enabled) {\n const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n const data = await emotionT.data();\n tf.dispose(emotionT);\n for (let i = 0; i < data.length; i++) {\n if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n tf.dispose(normalize);\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\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 count = partNames.length; // 17 keypoints\n\nexport const partIds = 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", "import * as kpt from './keypoints';\nimport { Body } from '../result';\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\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = 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: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i) => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight],\n })),\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: Array; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\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() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\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 // @ts-ignore getter is of unknown type\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\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.count),\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 utils from './utils';\nimport * as kpt from './keypoints';\n\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: kpt.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = kpt.poseChain.map(([parentJoinName, childJoinName]) => ([kpt.partIds[parentJoinName], kpt.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: kpt.partNames[root.part.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 sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, 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 sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, 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(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.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 < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: Array<{ keypoints, box: [number, number, number, number], score: number }> = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !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 // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n", "/**\n * PoseNet module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as poses from './poses';\nimport * as util from './utils';\nimport { Body } from '../result';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Array = model.execute(normalized, poseNetOutputs) as Array;\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = results3d[1].sigmoid(); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = await poses.decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = util.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as Body[];\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch for GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\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", "export const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 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0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 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0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.609375, y: 0.921875 },\n { x: 0.609375, y: 0.921875 },\n { x: 0.640625, y: 0.921875 },\n { x: 0.640625, y: 0.921875 },\n { x: 0.671875, y: 0.921875 },\n { x: 0.671875, y: 0.921875 },\n { x: 0.703125, y: 0.921875 },\n { x: 0.703125, y: 0.921875 },\n { x: 0.734375, y: 0.921875 },\n { x: 0.734375, y: 0.921875 },\n { x: 0.765625, y: 0.921875 },\n { x: 0.765625, y: 0.921875 },\n { x: 0.796875, y: 0.921875 },\n { x: 0.796875, y: 0.921875 },\n { x: 0.828125, y: 0.921875 },\n { x: 0.828125, y: 0.921875 },\n { x: 0.859375, y: 0.921875 },\n { x: 0.859375, y: 0.921875 },\n { x: 0.890625, y: 0.921875 },\n { x: 0.890625, y: 0.921875 },\n { x: 0.921875, y: 0.921875 },\n { x: 0.921875, y: 0.921875 },\n { x: 0.953125, y: 0.921875 },\n { x: 0.953125, y: 0.921875 },\n { x: 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0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.3125, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.4375, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.5625, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.6875, y: 0.5625 },\n { x: 0.8125, y: 0.5625 },\n { x: 0.8125, y: 0.5625 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0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as anchors from './anchors';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n // @ts-ignore model is not undefined here\n this.inputSize = this.model?.inputs[0].shape[2];\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.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(tf.reshape(rawPalmLandmarks, [-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) as Tensor;\n const predictions = tf.squeeze(batched);\n tf.dispose(batched);\n const scoresT = tf.tidy(() => tf.squeeze(tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1]))));\n const scores = await scoresT.data();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n tf.dispose(rawBoxes);\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n const filtered = await filteredT.array();\n\n tf.dispose(scoresT);\n tf.dispose(filteredT);\n const hands: Array<{ box: Tensor, palmLandmarks: Tensor, 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(() => tf.reshape(this.normalizeLandmarks(rawPalmLandmarks, index), [-1, 2]));\n tf.dispose(rawPalmLandmarks);\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n tf.dispose(predictions);\n tf.dispose(boxes);\n return hands;\n }\n\n async estimateHandBounds(input, config): Promise<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }[]> {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => tf.sub(tf.div(tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]), 127.5), 1));\n const predictions = await this.getBoxes(image, config);\n tf.dispose(image);\n const hands: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }> = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = await prediction.box.data();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = await prediction.palmLandmarks.array();\n tf.dispose(prediction.box);\n tf.dispose(prediction.palmLandmarks);\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.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';\nimport * as detector from './handdetector';\nimport { Tensor, GraphModel } from '../tfjs/types';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number } | null>;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n // @ts-ignore model is not undefined here\n this.inputSize = this.handPoseModel?.inputs[0].shape[2];\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\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 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(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[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 Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(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\n // console.log(this.skipped, config.hand.skipFrames, !config.hand.landmarks, !config.skipFrame);\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.skipFrame) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.skipFrame) 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.maxDetected) || !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<{ landmarks?: number[], confidence: number, box: { topLeft: number[], bottomRight: number[] } }> = [];\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[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 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.ENV.flags.IS_BROWSER ? 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 = tf.div(croppedInput, 255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage) as Array;\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\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 tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), handBoxEnlargeFactor);\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", "/**\n * HandPose module entry point\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport { Hand } from '../result';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Config } from '../config';\n\nconst meshAnnotations = {\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\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n // @ts-ignore landmarks are not undefined\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n\n const keypoints = predictions[i].landmarks as unknown as Array<[number, number, number]>;\n\n let box: [number, number, number, number] = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: [number, number, number, number] = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!handDetectorModel || !handPoseModel) {\n // @ts-ignore type mismatch on GraphModel\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n if (config.hand.enabled) {\n if (!handDetectorModel || !handDetectorModel['modelUrl']) log('load model failed:', config.hand.detector.modelPath);\n else if (config.debug) log('load model:', handDetectorModel['modelUrl']);\n if (!handPoseModel || !handPoseModel['modelUrl']) log('load model failed:', config.hand.skeleton.modelPath);\n else if (config.debug) log('load model:', handPoseModel['modelUrl']);\n }\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = new handdetector.HandDetector(handDetectorModel);\n handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "export const full = [\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 const upper = [\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 'left:15',\n 'right:16',\n 'left:17',\n 'right:18',\n 'left:19',\n 'right:20',\n 'left:21',\n 'right:22',\n 'leftChest',\n 'rightChest',\n 'neck',\n 'forehead',\n 'left:27',\n 'right:28',\n 'left:29',\n 'right:30',\n];\n", "/**\n * BlazePose Module\n */\n\n// paper: https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as annotations from './annotations';\nimport { Tensor, GraphModel } from '../tfjs/types';\nimport { Body } from '../result';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch for Graphmodel\n model = await tf.loadGraphModel(join(config.modelBasePath, 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 (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if (!model) return [];\n if (!config.body.enabled) return [];\n const imgSize = { width: (image.shape[2] || 0), height: (image.shape[1] || 0) };\n const resize = tf.image.resizeBilinear(image, [model['width'], model['height']], false);\n const normalize = tf.div(resize, [255.0]);\n tf.dispose(resize);\n const resT = await model.predict(normalize) as Array;\n const findT = resT.find((t) => (t.size === 195 || t.size === 155));\n const points = await findT?.data() || []; // order of output tensors may change between models, full has 195 and upper has 155 items\n resT.forEach((t) => tf.dispose(t));\n tf.dispose(normalize);\n const keypoints: Array<{ id, part, position: [number, number, number], positionRaw: [number, number, number], score, presence }> = [];\n const labels = points?.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n keypoints.push({\n id: i,\n part: labels[i],\n position: [\n Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n ],\n positionRaw: [\n points[depth * i + 0] / 255, // return x value normalized to 0..1\n points[depth * i + 1] / 255, // return y value normalized to 0..1\n points[depth * i + 2] + 0, // fix negative zero\n ],\n score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n });\n }\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n const box: [number, number, number, number] = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...x),\n ];\n const boxRaw: [number, number, number, number] = [0, 0, 0, 0]; // not yet implemented\n const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n return [{ id: 0, score, box, boxRaw, keypoints }];\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Body } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\ntype Keypoints = { score: number, part: string, position: [number, number], positionRaw: [number, number] };\n\nconst keypoints: Array = [];\nlet box: [number, number, number, number] = [0, 0, 0, 0];\nlet boxRaw: [number, number, number, number] = [0, 0, 0, 0];\nlet score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n const [width, height] = inputs.shape;\n return tf.tidy(() => {\n const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32'))); // modulus op implemented in tf\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const newScore = tf.max(reshaped, 0).dataSync()[0]; // get highest score // inside tf.tidy\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coords = tf.argMax(reshaped, 0);\n const x = mod(coords, width).dataSync()[0]; // inside tf.tidy\n const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0]; // inside tf.tidy\n return [x, y, newScore];\n }\n return [0, 0, newScore];\n });\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {\n skipped++;\n return [{ id: 0, score, box, boxRaw, keypoints }];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const enhance = tf.mul(resize, 2);\n const norm = enhance.sub(1);\n return norm;\n });\n\n let resT;\n if (config.body.enabled) resT = await model.predict(tensor);\n tf.dispose(tensor);\n\n if (resT) {\n keypoints.length = 0;\n const squeeze = resT.squeeze();\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = squeeze.unstack(2);\n tf.dispose(squeeze);\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = max2d(stack[id], config.body.minConfidence);\n if (score > config.body.minConfidence) {\n keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: bodyParts[id],\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = keypoints.map((a) => a.positionRaw[0]);\n const yRaw = keypoints.map((a) => a.positionRaw[1]);\n boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n resolve([{ id: 0, score, box, boxRaw, keypoints }]);\n });\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { Body } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model: GraphModel;\n\ntype Keypoints = { score: number, part: string, position: [number, number], positionRaw: [number, number] };\n\nconst keypoints: Array = [];\nlet box: [number, number, number, number] = [0, 0, 0, 0];\nlet boxRaw: [number, number, number, number] = [0, 0, 0, 0];\nlet score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder', 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist', 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle'];\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {\n skipped++;\n return [{ id: 0, score, box, boxRaw, keypoints }];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const cast = tf.cast(resize, 'int32');\n return cast;\n });\n\n let resT;\n if (config.body.enabled) resT = await model.predict(tensor);\n tf.dispose(tensor);\n\n if (resT) {\n keypoints.length = 0;\n const res = await resT.array();\n tf.dispose(resT);\n const kpt = res[0][0];\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: bodyParts[id],\n positionRaw: [ // normalized to 0..1\n kpt[id][1],\n kpt[id][0],\n ],\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * kpt[id][1]),\n Math.round((image.shape[1] || 0) * kpt[id][0]),\n ],\n });\n }\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = keypoints.map((a) => a.position[0]);\n const y = keypoints.map((a) => a.position[1]);\n box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = keypoints.map((a) => a.positionRaw[0]);\n const yRaw = keypoints.map((a) => a.positionRaw[1]);\n boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n resolve([{ id: 0, score, box, boxRaw, keypoints }]);\n });\n}\n", "/**\n * CoCo Labels used by object detection modules\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * NanoDet object detection module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { labels } from './labels';\nimport { Item } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model;\nlet last: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n const inputs = Object.values(model.modelSignature['inputs']);\n model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n let id = 0;\n let results: Array = [];\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > config.object.minConfidence && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: (box.map((a) => Math.trunc(a))) as [number, number, number, number],\n boxRaw: boxRaw as [number, number, number, number],\n };\n results.push(result);\n }\n }\n }\n });\n }\n // deallocate tensors\n res.forEach((t) => tf.dispose(t));\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: Array = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = await nms.data();\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2], image.shape[1]];\n const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n const norm = tf.div(resize, 255);\n const transpose = norm.transpose([0, 3, 1, 2]);\n tf.dispose(norm);\n tf.dispose(resize);\n\n let objectT;\n if (config.object.enabled) objectT = await model.predict(transpose);\n tf.dispose(transpose);\n\n const obj = await process(objectT, model.inputSize, outputSize, config);\n last = obj;\n resolve(obj);\n });\n}\n", "/**\n * CenterNet object detection module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { labels } from './labels';\nimport { Item } from '../result';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\nlet model;\nlet last: Item[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n const inputs = Object.values(model.modelSignature['inputs']);\n model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n else if (config.debug) log('load model:', model.modelUrl);\n } else if (config.debug) log('cached model:', model.modelUrl);\n return model;\n}\n\nasync function process(res: Tensor, inputSize, outputShape, config: Config) {\n if (!res) return [];\n const results: Array = [];\n const detections = await res.array();\n const squeezeT = tf.squeeze(res);\n tf.dispose(res);\n const arr = tf.split(squeezeT, 6, 1); // x1, y1, x2, y2, score, class\n tf.dispose(squeezeT);\n const stackT = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n const boxesT = tf.squeeze(stackT);\n const scoresT = tf.squeeze(arr[4]);\n const classesT = tf.squeeze(arr[5]);\n arr.forEach((t) => tf.dispose(t));\n const nmsT = await tf.image.nonMaxSuppressionAsync(boxesT, scoresT, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n tf.dispose(boxesT);\n tf.dispose(scoresT);\n tf.dispose(classesT);\n const nms = await nmsT.data();\n tf.dispose(nmsT);\n let i = 0;\n for (const id of nms) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n const label = labels[classVal].label;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ] as [number, number, number, number];\n const box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ] as [number, number, number, number];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2], input.shape[1]];\n const resize = tf.image.resizeBilinear(input, [model.inputSize, model.inputSize]);\n const objectT = config.object.enabled ? model.execute(resize, ['tower_0/detections']) : null;\n tf.dispose(resize);\n\n const obj = await process(objectT, model.inputSize, outputSize, config);\n last = obj;\n resolve(obj);\n });\n}\n", "/*\nWebGLImageFilter by Dominic Szablewski: \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)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\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)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\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 _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 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 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", "/**\n * Image Processing module used by Human\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport { Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\ntype Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\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: Input, config: Config): { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n let tensor;\n if (!input) throw new Error('Human: Input is missing');\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('Human: Input type is not recognized');\n }\n if (input instanceof tf.Tensor) {\n // if input is tensor, use as-is\n if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n } else {\n // check if resizing will be needed\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 if (!originalWidth || !originalHeight) return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = targetWidth * originalHeight / originalWidth;\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = targetHeight * originalWidth / originalHeight;\n }\n\n // create our canvas and resize it if needed\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) throw new Error('Human: Input cannot determine dimension');\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\n // draw input to our canvas\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) {\n ctx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof ctx.translate !== 'undefined') {\n ctx.translate(originalWidth, 0);\n ctx.scale(-1, 1);\n ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n ctx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n }\n }\n\n // imagefx transforms using gl\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 { tensor: null, canvas: 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 const shape = [outCanvas.height, outCanvas.width, 3];\n const pixels = tf.tensor3d(outCanvas.data, shape, 'float32');\n tensor = tf.expandDims(pixels, 0);\n tf.dispose(pixels);\n }\n */\n } else {\n outCanvas = inCanvas;\n if (fx) fx = null;\n }\n\n // create tensor from image if tensor is not already defined\n if (!tensor) {\n let pixels;\n if (outCanvas.data) { // if we have data, just convert to tensor\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n pixels = tf.browser ? tf.browser.fromPixels(outCanvas) : null;\n } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n // we cant use canvas as-is as it already has a context, 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 pixels = tf.browser ? tf.browser.fromPixels(tempCanvas) : null;\n } else { // cpu and wasm kernel does not implement efficient fromPixels method\n // we cant use canvas as-is as it already has a context, so we do a silly one more canvas and do fromPixels on ImageData instead\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 ? tf.browser.fromPixels(data) : null;\n }\n if (pixels) {\n const casted = tf.cast(pixels, 'float32');\n tensor = tf.expandDims(casted, 0);\n tf.dispose(pixels);\n tf.dispose(casted);\n }\n }\n }\n const canvas = config.filter.return ? outCanvas : null;\n return { tensor, canvas };\n}\n", "/**\n * EfficientPose Module\n */\n\nimport { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport { GraphModel, Tensor } from '../tfjs/types';\nimport { Config } from '../config';\n\ntype Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise {\n if (!model) {\n // @ts-ignore type mismatch on GraphModel\n model = await tf.loadGraphModel(join(config.modelBasePath, config.segmentation.modelPath));\n if (!model || !model['modelUrl']) log('load model failed:', config.segmentation.modelPath);\n else if (config.debug) log('load model:', model['modelUrl']);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement }): Promise {\n const width = input.tensor?.shape[1] || 0;\n const height = input.tensor?.shape[2] || 0;\n if (!input.tensor) return null;\n if (!model || !model.inputs[0].shape) return null;\n const resizeInput = tf.image.resizeBilinear(input.tensor, [model.inputs[0].shape[1], model.inputs[0].shape[2]], false);\n const norm = tf.div(resizeInput, 255);\n const res = model.predict(norm) as Tensor;\n // meet output: 1,256,256,1\n // selfie output: 1,144,256,2\n tf.dispose(resizeInput);\n tf.dispose(norm);\n\n const squeeze = tf.squeeze(res, 0);\n let resizeOutput;\n if (squeeze.shape[2] === 2) {\n // model meet has two channels for fg and bg\n const softmax = squeeze.softmax();\n const [bg, fg] = tf.unstack(softmax, 2);\n const expand = tf.expandDims(fg, 2);\n const pad = tf.expandDims(expand, 0);\n tf.dispose(softmax);\n tf.dispose(bg);\n tf.dispose(fg);\n // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n const crop = tf.image.cropAndResize(pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n // otherwise run softmax after unstack and use standard resize\n // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n resizeOutput = tf.squeeze(crop, 0);\n tf.dispose(crop);\n tf.dispose(expand);\n tf.dispose(pad);\n } else { // model selfie has a single channel that we can use directly\n resizeOutput = tf.image.resizeBilinear(squeeze, [width, height]);\n }\n\n if (typeof document === 'undefined') return resizeOutput.data(); // we're running in nodejs so return alpha array as-is\n\n const overlay = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas');\n overlay.width = width;\n overlay.height = height;\n if (tf.browser) await tf.browser.toPixels(resizeOutput, overlay);\n tf.dispose(resizeOutput);\n tf.dispose(squeeze);\n tf.dispose(res);\n\n // get alpha channel data\n const alphaCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas'); // need one more copy since input may already have gl context so 2d context fails\n alphaCanvas.width = width;\n alphaCanvas.height = height;\n const ctxAlpha = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctxAlpha.filter = 'blur(8px';\n await ctxAlpha.drawImage(overlay, 0, 0);\n const alpha = ctxAlpha.getImageData(0, 0, width, height).data;\n\n // get original canvas merged with overlay\n const original = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas'); // need one more copy since input may already have gl context so 2d context fails\n original.width = width;\n original.height = height;\n const ctx = original.getContext('2d') as CanvasRenderingContext2D;\n if (input.canvas) await ctx.drawImage(input.canvas, 0, 0);\n // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n ctx.globalCompositeOperation = 'darken';\n ctx.filter = 'blur(8px)'; // use css filter for bluring, can be done with gaussian blur manually instead\n await ctx.drawImage(overlay, 0, 0);\n ctx.globalCompositeOperation = 'source-over'; // reset\n ctx.filter = 'none'; // reset\n\n input.canvas = original;\n\n return alpha;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config): Promise {\n if (busy) return null;\n busy = true;\n if (!model) await load(config);\n const img = image.process(input, config);\n const alpha = await predict(img);\n tf.dispose(img.tensor);\n\n if (background && alpha) {\n const tmp = image.process(background, config);\n const bg = tmp.canvas;\n tf.dispose(tmp.tensor);\n const fg = img.canvas;\n const fgData = fg.getContext('2d')?.getImageData(0, 0, fg.width, fg.height).data as Uint8ClampedArray;\n\n const c = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(fg.width, fg.height) : document.createElement('canvas');\n c.width = fg.width;\n c.height = fg.height;\n const ctx = c.getContext('2d') as CanvasRenderingContext2D;\n\n ctx.globalCompositeOperation = 'copy'; // reset\n ctx.drawImage(bg, 0, 0, c.width, c.height);\n const cData = ctx.getImageData(0, 0, c.width, c.height) as ImageData;\n for (let i = 0; i < c.width * c.height; i++) { // this should be done with globalCompositeOperation instead of looping through image data\n cData.data[4 * i + 0] = ((255 - alpha[4 * i + 0]) / 255.0 * cData.data[4 * i + 0]) + (alpha[4 * i + 0] / 255.0 * fgData[4 * i + 0]);\n cData.data[4 * i + 1] = ((255 - alpha[4 * i + 1]) / 255.0 * cData.data[4 * i + 1]) + (alpha[4 * i + 1] / 255.0 * fgData[4 * i + 1]);\n cData.data[4 * i + 2] = ((255 - alpha[4 * i + 2]) / 255.0 * cData.data[4 * i + 2]) + (alpha[4 * i + 2] / 255.0 * fgData[4 * i + 2]);\n cData.data[4 * i + 3] = ((255 - alpha[4 * i + 3]) / 255.0 * cData.data[4 * i + 3]) + (alpha[4 * i + 3] / 255.0 * fgData[4 * i + 3]);\n }\n ctx.putImageData(cData, 0, 0);\n img.canvas = c;\n }\n busy = false;\n return img.canvas;\n}\n", "import * as facemesh from './blazeface/facemesh';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as movenet from './movenet/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as centernet from './object/centernet';\nimport * as segmentation from './segmentation/segmentation';\n// import * as agegenderrace from './gear/agegenderrace';\n\n/** Load method preloads all instance.configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n * @param userinstance.config?: {@link instance.config}\n*/\nexport async function load(instance) {\n if (instance.config.async) { // load models concurrently\n [\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.face,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.emotion,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.handpose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.posenet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.blazepose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.efficientpose,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.movenet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.nanodet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.centernet,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.faceres,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n instance.models.segmentation,\n // @ts-ignore models loaded via promise array cannot be correctly inferred\n // instance.models.agegenderrace,\n ] = await Promise.all([\n instance.models.face || (instance.config.face.enabled ? facemesh.load(instance.config) : null),\n instance.models.emotion || ((instance.config.face.enabled && instance.config.face.emotion.enabled) ? emotion.load(instance.config) : null),\n instance.models.handpose || (instance.config.hand.enabled ? handpose.load(instance.config) : null),\n instance.models.posenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('posenet') ? posenet.load(instance.config) : null),\n instance.models.blazepose || (instance.config.body.enabled && instance.config.body.modelPath.includes('blazepose') ? blazepose.load(instance.config) : null),\n instance.models.efficientpose || (instance.config.body.enabled && instance.config.body.modelPath.includes('efficientpose') ? efficientpose.load(instance.config) : null),\n instance.models.movenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('movenet') ? movenet.load(instance.config) : null),\n instance.models.nanodet || (instance.config.object.enabled && instance.config.object.modelPath.includes('nanodet') ? nanodet.load(instance.config) : null),\n instance.models.centernet || (instance.config.object.enabled && instance.config.object.modelPath.includes('centernet') ? centernet.load(instance.config) : null),\n instance.models.faceres || ((instance.config.face.enabled && instance.config.face.description.enabled) ? faceres.load(instance.config) : null),\n instance.models.segmentation || (instance.config.segmentation.enabled ? segmentation.load(instance.config) : null),\n // instance.models.agegenderrace || ((instance.config.face.enabled && instance.config.face.agegenderrace.enabled) ? agegenderrace.load(instance.config) : null),\n ]);\n } else { // load models sequentially\n if (instance.config.face.enabled && !instance.models.face) instance.models.face = await facemesh.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.emotion.enabled && !instance.models.emotion) instance.models.emotion = await emotion.load(instance.config);\n if (instance.config.hand.enabled && !instance.models.handpose) instance.models.handpose = await handpose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath.includes('posenet')) instance.models.posenet = await posenet.load(instance.config);\n if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath.includes('blazepose')) instance.models.blazepose = await blazepose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath.includes('efficientpose')) instance.models.efficientpose = await blazepose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath.includes('movenet')) instance.models.movenet = await movenet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath.includes('nanodet')) instance.models.nanodet = await nanodet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath.includes('centernet')) instance.models.centernet = await centernet.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.description.enabled && !instance.models.faceres) instance.models.faceres = await faceres.load(instance.config);\n if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = await segmentation.load(instance.config);\n // if (instance.config.face.enabled && instance.config.face.agegenderrace.enabled && !instance.models.agegenderrace) instance.models.agegenderrace = await agegenderrace.load(instance.config);\n }\n}\n", "/**\n * Module that analyzes person age\n * Obsolete\n */\n\nimport { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as facemesh from './blazeface/facemesh';\nimport * as emotion from './emotion/emotion';\nimport * as faceres from './faceres/faceres';\nimport { Face } from './result';\nimport { Tensor } from './tfjs/types';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nconst rad2deg = (theta) => Math.round((theta * 180) / Math.PI);\n\nconst calculateGaze = (face): { bearing: number, strength: number } => {\n const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = face.mesh[33][2] > face.mesh[263][2]; // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n\n const eyeDiff = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] ** 2) + (eyeDiff[1] ** 2)); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n\n return { bearing, strength };\n};\n\nconst calculateFaceAngle = (face, imageSize): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v) => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a, b) => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a, b) => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r) => {\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n let thetaX; let thetaY; let thetaZ;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n // simple Euler angle calculation based existing 3D mesh\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const meshToEulerAngle = (mesh) => {\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const angle = {\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n // pitch is face move up/down\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n // yaw is face turn left/right\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n // roll is face lean left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n };\n return angle;\n };\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n // make the xyz coordinates proportional, independent of the image/box size\n pt[0] * imageSize[0] / size,\n pt[1] * imageSize[1] / size,\n pt[2],\n ]);\n\n const y_axis = normalize(subVectors(pts[1], pts[0]));\n let x_axis = normalize(subVectors(pts[3], pts[2]));\n const z_axis = normalize(crossVectors(x_axis, y_axis));\n // adjust x_axis to make sure that all axes are perpendicular to each other\n x_axis = crossVectors(y_axis, z_axis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n x_axis[0], x_axis[1], x_axis[2],\n y_axis[0], y_axis[1], y_axis[2],\n z_axis[0], z_axis[1], z_axis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n\nexport const detectFace = async (parent /* instance of human */, input: Tensor): Promise => {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let gearRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n let descRes;\n const faceRes: Array = [];\n parent.state = 'run:face';\n timeStamp = now();\n const faces = await facemesh.predict(input, parent.config);\n parent.performance.face = Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n parent.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefined\n if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);\n\n // run emotion, inherits face from blazeface\n parent.analyze('Start Emotion:');\n if (parent.config.async) {\n emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n } else {\n parent.state = 'run:emotion';\n timeStamp = now();\n emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n parent.performance.emotion = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End Emotion:');\n\n // run gear, inherits face from blazeface\n /*\n parent.analyze('Start GEAR:');\n if (parent.config.async) {\n gearRes = parent.config.face.agegenderrace.enabled ? agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n } else {\n parent.state = 'run:gear';\n timeStamp = now();\n gearRes = parent.config.face.agegenderrace.enabled ? await agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};\n parent.performance.emotion = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End GEAR:');\n */\n\n // run emotion, inherits face from blazeface\n parent.analyze('Start Description:');\n if (parent.config.async) {\n descRes = parent.config.face.description.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];\n } else {\n parent.state = 'run:description';\n timeStamp = now();\n descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];\n parent.performance.embedding = Math.trunc(now() - timeStamp);\n }\n parent.analyze('End Description:');\n\n // if async wait for results\n if (parent.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes]);\n }\n\n parent.analyze('Finish Face:');\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {\n delete faces[i].annotations.leftEyeIris;\n delete faces[i].annotations.rightEyeIris;\n }\n const irisSize = (faces[i].annotations?.leftEyeIris && faces[i].annotations?.rightEyeIris)\n /* note: average human iris size is 11.7mm */\n ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n : 0;\n\n // optionally return tensor\n const tensor = parent.config.face.detector.return ? tf.squeeze(faces[i].tensor) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n faceRes.push({\n ...faces[i],\n id: i,\n age: descRes.age,\n gender: descRes.gender,\n genderScore: descRes.genderScore,\n embedding: descRes.descriptor,\n emotion: emotionRes,\n iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,\n rotation,\n tensor,\n });\n\n parent.analyze('End Face');\n }\n parent.analyze('End FaceMesh:');\n if (parent.config.async) {\n if (parent.performance.face) delete parent.performance.face;\n if (parent.performance.age) delete parent.performance.age;\n if (parent.performance.gender) delete parent.performance.gender;\n if (parent.performance.emotion) delete parent.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * Gesture detection module\n */\n\nimport { Gesture } from '../result';\n\n/**\n * @typedef FaceGesture\n */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/**\n * @typedef IrisGesture\n */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/**\n * @typedef BodyGesture\n */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/**\n * @typedef BodyGesture\n */\nexport type HandGesture =\n `${'thumb' | 'index finger' | 'middle finger' | 'ring finger' | 'pinky'} forward`\n | `${'thumb' | 'index finger' | 'middle finger' | 'ring finger' | 'pinky'} up`;\n\nexport const body = (res): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\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): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\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[33][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'left' : 'right'}` });\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): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\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 let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > 0.06) gestures.push({ iris: i, gesture: 'looking right' });\n if (rightIrisCenterX > 0.06) gestures.push({ iris: i, gesture: 'looking left' });\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res): Gesture[] => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\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 if (finger !== 'palmBase' && Array.isArray(pos)) 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 gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n }\n return gestures;\n};\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep, now } from '../helpers';\nimport type { Result, Face, Body, Hand, Item, Gesture, Person } from '../result';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n */\nexport interface DrawOptions {\n color: string,\n labelColor: string,\n shadowColor: string,\n font: string,\n lineHeight: number,\n lineWidth: number,\n pointSize: number,\n roundRect: number,\n drawPoints: boolean,\n drawLabels: boolean,\n drawBoxes: boolean,\n drawPolygons: boolean,\n drawGaze: boolean,\n fillPolygons: boolean,\n useDepth: boolean,\n useCurves: boolean,\n bufferedOutput: boolean,\n}\n\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n shadowColor: 'black',\n font: 'small-caps 14px \"Segoe UI\"',\n lineHeight: 18,\n lineWidth: 4,\n pointSize: 2,\n roundRect: 8,\n drawPoints: false,\n drawLabels: true,\n drawBoxes: true,\n drawPolygons: true,\n drawGaze: true,\n fillPolygons: false,\n useDepth: true,\n useCurves: false,\n bufferedOutput: true,\n};\n\nconst rad2deg = (theta) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n ctx.beginPath();\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.lineWidth = localOptions.lineWidth;\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nfunction lines(ctx, points: [number, number, number?][] = [], localOptions) {\n if (points === undefined || points.length === 0) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n const z = pt[2] || 0;\n ctx.strokeStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n ctx.lineTo(pt[0], Math.round(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nfunction curves(ctx, points: [number, number, number?][] = [], localOptions) {\n if (points === undefined || points.length === 0) return;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n let where: unknown[] = []; // what&where is a record\n let what: unknown[] = []; // what&where is a record\n [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${who}: ${what[1]}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n i += 1;\n }\n }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n for (const f of result) {\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n // silly hack since fillText does not suport new line\n const labels:string[] = [];\n labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n if (f.age) labels.push(`age: ${f.age || ''}`);\n if (f.iris) labels.push(`distance: ${f.iris}`);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n labels.push(emotion.join(' '));\n }\n if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = localOptions.color;\n for (let i = labels.length - 1; i >= 0; i--) {\n const x = Math.max(f.box[0], 0);\n const y = i * localOptions.lineHeight + f.box[1];\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(labels[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(labels[i], x + 4, y + 15);\n }\n ctx.lineWidth = 1;\n if (f.mesh && f.mesh.length > 0) {\n if (localOptions.drawPoints) {\n for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n }\n if (localOptions.drawPolygons) {\n ctx.lineWidth = 1;\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) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n // iris: array[center, left, top, right, bottom]\n if (f.annotations && f.annotations['leftEyeIris']) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations && f.annotations['rightEyeIris']) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = 'pink';\n ctx.beginPath();\n\n const leftGaze = [\n f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n ctx.moveTo(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]);\n ctx.lineTo(leftGaze[0], leftGaze[1]);\n\n const rightGaze = [\n f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n ctx.moveTo(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]);\n ctx.lineTo(rightGaze[0], rightGaze[1]);\n\n ctx.stroke();\n }\n }\n }\n }\n}\n\nexport async function body(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n // @ts-ignore box may not exist\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n // @ts-ignore box may not exist\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n // @ts-ignore box may not exist\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n }\n if (localOptions.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels) {\n ctx.font = localOptions.font;\n if (result[i].keypoints) {\n for (const pt of result[i].keypoints) {\n ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n }\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints) {\n let part;\n const points: [number, number, number?][] = [];\n // shoulder line\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // torso main\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n // leg left\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // leg right\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // arm left\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // arm right\n points.length = 0;\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) points.push([part.position[0], part.position[1]]);\n part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n if (part) points.push([part.position[0], part.position[1]]);\n curves(ctx, points, localOptions);\n // draw all\n }\n }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels) {\n const addHandLabel = (part, title) => {\n ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[part.length - 1][2])}, ${127.5 - (2 * part[part.length - 1][2])}, 255, 0.5)` : localOptions.color;\n ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n };\n ctx.font = localOptions.font;\n addHandLabel(h.annotations['indexFinger'], 'index');\n addHandLabel(h.annotations['middleFinger'], 'middle');\n addHandLabel(h.annotations['ringFinger'], 'ring');\n addHandLabel(h.annotations['pinky'], 'pinky');\n addHandLabel(h.annotations['thumb'], 'thumb');\n addHandLabel(h.annotations['palmBase'], 'palm');\n }\n if (localOptions.drawPolygons) {\n const addHandLine = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations['indexFinger']);\n addHandLine(h.annotations['middleFinger']);\n addHandLine(h.annotations['ringFinger']);\n addHandLine(h.annotations['pinky']);\n addHandLine(h.annotations['thumb']);\n // addPart(h.annotations.palmBase);\n }\n }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `${h.label} ${Math.round(100 * h.score)}%`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\nexport async function person(inCanvas: HTMLCanvasElement, result: Array, drawOptions?: DrawOptions) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n const ctx = inCanvas.getContext('2d');\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n if (!inCanvas || !outCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n const outCtx = inCanvas.getContext('2d');\n outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result: Result, drawOptions?: DrawOptions) {\n const timestamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (!(inCanvas instanceof HTMLCanvasElement)) return;\n\n face(inCanvas, result.face, localOptions);\n body(inCanvas, result.body, localOptions);\n hand(inCanvas, result.hand, localOptions);\n object(inCanvas, result.object, localOptions);\n // person(inCanvas, result.persons, localOptions);\n gesture(inCanvas, result.gesture, localOptions); // gestures do not have buffering\n\n /*\n if (!bufferedResult) bufferedResult = result; // first pass\n else if (localOptions.bufferedOutput) calcBuffered(result); // do results interpolation\n else bufferedResult = result; // or just use results as-is\n const promises: Promise[] = [];\n promises.push(face(inCanvas, bufferedResult.face, localOptions));\n promises.push(body(inCanvas, bufferedResult.body, localOptions));\n promises.push(hand(inCanvas, bufferedResult.hand, localOptions));\n promises.push(object(inCanvas, bufferedResult.object, localOptions));\n // promises.push(person(inCanvas, bufferedResult.persons, localOptions));\n promises.push(gesture(inCanvas, result.gesture, localOptions)); // gestures do not have buffering\n // await Promise.all(promises);\n */\n result.performance.draw = Math.trunc(now() - timestamp);\n}\n", "/**\n * Module that analyzes existing results and recombines them into a unified person object\n */\n\nimport { Face, Body, Hand, Gesture, Person } from './result';\n\nexport function join(faces: Array, bodies: Array, hands: Array, gestures: Array, shape: Array | undefined): Array {\n let id = 0;\n const persons: Array = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: Person = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n }\n\n // create new overarching box from all boxes beloning to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face?.box);\n extractXY(person.body?.box);\n extractXY(person.hands?.left?.box);\n extractXY(person.hands?.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape && shape.length === 4) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Module that interpolates results for smoother animations\n */\n\nimport type { Result, Face, Body, Hand, Item, Gesture, Person } from './result';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };\n\nexport function calc(newResult: Result): Result {\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n // curve fitted: buffer = 8 - ln(delay)\n // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n // - at 50ms delay buffer = ~4.1 => 28% towards live data\n // - at 250ms delay buffer = ~2.5 => 40% towards live data\n // - at 500ms delay buffer = ~1.8 => 55% towards live data\n // - at 750ms delay buffer = ~1.4 => 71% towards live data\n // - at 1sec delay buffer = 1 which means live data is used\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed) : 1;\n\n bufferedResult.canvas = newResult.canvas;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as Body[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + b) / bufferedFactor) as [number, number, number, number];\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + b) / bufferedFactor) as [number, number, number, number];\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((keypoint, j) => ({\n score: keypoint.score,\n part: keypoint.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[0] + keypoint.position[0]) / bufferedFactor : keypoint.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[1] + keypoint.position[1]) / bufferedFactor : keypoint.position[1],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[0] + keypoint.positionRaw[0]) / bufferedFactor : keypoint.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[1] + keypoint.positionRaw[1]) / bufferedFactor : keypoint.position[1],\n ],\n }))) as Array<{ score: number, part: string, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as Hand[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n const keypoints = newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * bufferedResult.hand[i].keypoints[j][k] + coord) / bufferedFactor)) as [number, number, number]);\n const keys = Object.keys(newResult.hand[i].annotations); // update annotations\n const annotations = {};\n for (const key of keys) {\n annotations[key] = newResult.hand[i].annotations[key]\n .map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor));\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as Face[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as Item[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as Person[]));\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as [number, number, number, number];\n }\n }\n }\n\n // just copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture as Gesture[];\n if (newResult.performance) bufferedResult.performance = newResult.performance;\n\n return bufferedResult;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Human main module\n */\n\nimport { log, now, mergeDeep } from './helpers';\nimport { Config, defaults } from './config';\nimport { Result, Gesture } from './result';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as models from './models';\nimport * as face from './face';\nimport * as facemesh from './blazeface/facemesh';\nimport * as faceres from './faceres/faceres';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as movenet from './movenet/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as centernet from './object/centernet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as persons from './persons';\nimport * as interpolate from './interpolate';\nimport * as sample from './sample';\nimport * as app from '../package.json';\nimport { Tensor, GraphModel } from './tfjs/types';\n\n// export types\nexport type { Config } from './config';\nexport type { Result, Face, Hand, Body, Item, Gesture, Person } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection\n * @typedef Input Type\n */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message\n * @typedef Error Type\n */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS\n * @external\n */\nexport type TensorFlow = typeof tf;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig: {@link Config}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n /** Current configuration\n * - Details: {@link Config}\n */\n config: Config;\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n /** @internal: Instance of current image being processed */\n image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n /** @internal: Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n */\n tf: TensorFlow;\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - options: {@link DrawOptions} global settings for all draw operations, can be overriden for each draw method\n * - face: draw detected faces\n * - body: draw detected people and body parts\n * - hand: draw detected hands and hand parts\n * - canvas: draw processed canvas which is a processed copy of the input\n * - all: meta-function that performs: canvas, face, body, hand\n */\n draw: {\n options: draw.DrawOptions,\n gesture: typeof draw.gesture,\n face: typeof draw.face,\n body: typeof draw.body,\n hand: typeof draw.hand,\n canvas: typeof draw.canvas,\n all: typeof draw.all,\n };\n /** @internal: Currently loaded models */\n models: {\n face: [unknown, GraphModel | null, GraphModel | null] | null,\n posenet: GraphModel | null,\n blazepose: GraphModel | null,\n efficientpose: GraphModel | null,\n movenet: GraphModel | null,\n handpose: [GraphModel | null, GraphModel | null] | null,\n age: GraphModel | null,\n gender: GraphModel | null,\n emotion: GraphModel | null,\n embedding: GraphModel | null,\n nanodet: GraphModel | null,\n centernet: GraphModel | null,\n faceres: GraphModel | null,\n segmentation: GraphModel | null,\n };\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: typeof facemesh.triangulation;\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: typeof facemesh.uvmap;\n /** Platform and agent information detected by Human */\n sysinfo: { platform: string, agent: string };\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n #firstRun: boolean;\n #lastInputSum: number;\n #lastCacheDiff: number;\n\n // definition end\n\n /**\n * Creates instance of Human library that is futher used for all operations\n * @param userConfig: {@link Config}\n */\n constructor(userConfig?: Config | Record) {\n this.config = mergeDeep(defaults, userConfig || {});\n this.tf = tf;\n this.draw = draw;\n this.version = app.version;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.#firstRun = true;\n this.#lastCacheDiff = 0;\n this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };\n // object that contains all initialized models\n this.models = {\n face: null,\n posenet: null,\n blazepose: null,\n efficientpose: null,\n movenet: null,\n handpose: null,\n age: null,\n gender: null,\n emotion: null,\n embedding: null,\n nanodet: null,\n centernet: null,\n faceres: null,\n segmentation: null,\n };\n // export access to image processing\n // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n this.image = (input: Input) => image.process(input, this.config);\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // include platform info\n this.sysinfo = sysinfo.info();\n this.#lastInputSum = 1;\n }\n\n // helper function: measure tensor leak\n /** @hidden */\n analyze = (...msg) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n /** @hidden */\n #sanity = (input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n * - Calculation is based on normalized Minkowski distance between\n *\n * @param embedding1: face descriptor as array of numbers\n * @param embedding2: face descriptor as array of numbers\n * @returns similarity: number\n */\n // eslint-disable-next-line class-methods-use-this\n similarity(embedding1: Array, embedding2: Array): number {\n return faceres.similarity(embedding1, embedding2);\n }\n\n /**\n * Segmentation method takes any input and returns processed canvas with body segmentation\n * Optional parameter background is used to fill the background with specific input\n * Segmentation is not triggered as part of detect process\n *\n * @param input: {@link Input}\n * @param background?: {@link Input}\n * @returns Canvas\n */\n segmentation(input: Input, background?: Input) {\n return segmentation.process(input, background, this.config);\n }\n\n /** Enhance method performs additional enhacements to face image previously detected for futher processing\n * @param input: Tensor as provided in human.result.face[n].tensor\n * @returns Tensor\n */\n // eslint-disable-next-line class-methods-use-this\n enhance(input: Tensor): Tensor | null {\n // @ts-ignore type mismach for Tensor\n return faceres.enhance(input);\n }\n\n /** Math method find best match between provided face descriptor and predefined database of known descriptors\n * @param faceEmbedding: face descriptor previsouly calculated on any face\n * @param db: array of mapping of face descriptors to known values\n * @param threshold: minimum score for matching to be considered in the result\n * @returns best match\n */\n // eslint-disable-next-line class-methods-use-this\n match(faceEmbedding: Array, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n return faceres.match(faceEmbedding, db, threshold);\n }\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n * @param userConfig?: {@link Config}\n */\n async load(userConfig?: Config | Record) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.#firstRun) { // print version info on first run and check for correct backend setup\n if (this.config.debug) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n if (this.config.debug) log('platform:', this.sysinfo.platform);\n if (this.config.debug) log('agent:', this.sysinfo.agent);\n\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\n await models.load(this); // actually loads models\n\n if (this.#firstRun) { // print memory stats on first run\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.performance.load as number || 0)) this.performance.load = current;\n }\n\n // check if backend needs initialization if it changed\n /** @hidden */\n #checkBackend = async (force = false) => {\n if (this.config.backend && (this.config.backend.length > 0) && 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.length > 0) {\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && this.config.debug) log('running inside web worker');\n\n // force browser vs node backend\n if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') {\n if (this.config.debug) log('override: backend set to tensorflow while running in browser');\n this.config.backend = 'humangl';\n }\n if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) {\n if (this.config.debug) log('override: backend set to webgl while running in nodejs');\n this.config.backend = 'tensorflow';\n }\n\n const available = Object.keys(this.tf.engine().registryFactory);\n if (this.config.debug) log('available backends:', available);\n\n if (!available.includes(this.config.backend)) {\n log(`error: backend ${this.config.backend} not found in registry`);\n this.config.backend = this.tf.ENV.flags.IS_NODE ? 'tensorflow' : 'humangl';\n log(`override: using backend ${this.config.backend} instead`);\n }\n\n if (this.config.debug) log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n if (this.config.debug) log('wasm path:', this.config.wasmPath);\n if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n else throw new Error('Human: WASM backend is not loaded');\n const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') backend.register();\n try {\n await this.tf.setBackend(this.config.backend);\n } catch (err) {\n log('error: cannot set backend:', this.config.backend, err);\n }\n }\n this.tf.enableProdMode();\n // this.tf.enableDebugMode();\n if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n this.tf.ENV.set('WEBGL_CPU_FORWARD', true);\n this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n this.tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n // if (!this.config.object.enabled) this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (typeof this.config['deallocate'] !== 'undefined' && this.config['deallocate']) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n // @ts-ignore getGPGPUContext only exists on WebGL backend\n const gl = await this.tf.backend().getGPGPUContext().gl;\n if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await this.tf.ready();\n this.performance.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n /**\n * Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result?: {@link Result} optional use specific result set to run interpolation on\n * @returns result: {@link Result}\n */\n next = (result?: Result) => interpolate.calc(result || this.result) as Result;\n\n // check if input changed sufficiently to trigger new detections\n /** @hidden */\n #skipFrame = async (input) => {\n if (this.config.cacheSensitivity === 0) return false;\n const resizeFact = 32;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc(input.shape[1] / resizeFact), Math.trunc(input.shape[2] / resizeFact)]);\n // use tensor sum\n /*\n const sumT = this.tf.sum(reduced);\n const sum = await sumT.data()[0] as number;\n sumT.dispose();\n */\n // use js loop sum, faster than uploading tensor to gpu calculating and downloading back\n const reducedData = await reduced.data(); // raw image rgb array\n let sum = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum += reducedData[3 * i + 2]; // look only at green value of each pixel\n\n reduced.dispose();\n const diff = 100 * (Math.max(sum, this.#lastInputSum) / Math.min(sum, this.#lastInputSum) - 1);\n this.#lastInputSum = sum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n const skipFrame = diff < Math.max(this.config.cacheSensitivity, this.#lastCacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n this.#lastCacheDiff = diff > 10 * this.config.cacheSensitivity ? 0 : diff;\n return skipFrame;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input: Input\n * @param userConfig?: {@link Config}\n * @returns result: {@link Result}\n */\n async detect(input: Input, userConfig?: Config | Record): Promise {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n let elapsedTime;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\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 const timeStart = now();\n\n // configure backend\n await this.#checkBackend();\n\n // load models if enabled\n await this.load();\n\n /*\n // function disabled in favor of inputChanged\n // disable video optimization for inputs of type image, but skip if inside worker thread\n let previousVideoOptimized;\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (input && this.config.videoOptimized && (typeof window !== 'undefined') && (typeof WorkerGlobalScope !== 'undefined') && (\n (typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n || (typeof Image !== 'undefined' && input instanceof Image)\n || (typeof ImageData !== 'undefined' && input instanceof ImageData)\n || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap))\n ) {\n log('disabling video optimization');\n previousVideoOptimized = this.config.videoOptimized;\n this.config.videoOptimized = false;\n }\n */\n\n timeStamp = now();\n let process = image.process(input, this.config);\n this.performance.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run segmentation preprocessing\n if (this.config.segmentation.enabled && process && process.tensor) {\n this.analyze('Start Segmentation:');\n this.state = 'run:segmentation';\n timeStamp = now();\n await segmentation.predict(process);\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.segmentation = elapsedTime;\n if (process.canvas) {\n // replace input\n tf.dispose(process.tensor);\n process = image.process(process.canvas, this.config);\n }\n this.analyze('End Segmentation:');\n }\n\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\n timeStamp = now();\n this.config.skipFrame = await this.#skipFrame(process.tensor);\n if (!this.performance.frames) this.performance.frames = 0;\n if (!this.performance.cached) this.performance.cached = 0;\n (this.performance.frames as number)++;\n if (this.config.skipFrame) this.performance.cached++;\n this.performance.changed = Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes;\n let bodyRes;\n let handRes;\n let objectRes;\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 ? face.detectFace(this, process.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, process.tensor) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.face = elapsedTime;\n }\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n if (this.config.async) {\n if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(process.tensor, this.config) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.body = elapsedTime;\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 ? handpose.predict(process.tensor, this.config) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await handpose.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.hand = elapsedTime;\n }\n this.analyze('End Hand:');\n\n // run nanodet\n this.analyze('Start Object:');\n if (this.config.async) {\n if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(process.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n this.state = 'run:object';\n timeStamp = now();\n if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(process.tensor, this.config) : [];\n elapsedTime = Math.trunc(now() - timeStamp);\n if (elapsedTime > 0) this.performance.object = elapsedTime;\n }\n this.analyze('End Object:');\n\n // if async wait for results\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n let gestureRes: Gesture[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.performance.gesture = Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n this.result = {\n face: faceRes,\n body: bodyRes,\n hand: handRes,\n gesture: gestureRes,\n object: objectRes,\n performance: this.performance,\n canvas: process.canvas,\n timestamp: Date.now(),\n get persons() { return persons.join(faceRes, bodyRes, handRes, gestureRes, process?.tensor?.shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(process.tensor);\n\n // log('Result:', result);\n resolve(this.result);\n });\n }\n\n /** @hidden */\n #warmupBitmap = async () => {\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 /** @hidden */\n #warmupCanvas = async () => 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-upper.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 /** @hidden */\n #warmupNode = async () => {\n const atob = (str) => Buffer.from(str, 'base64');\n let img;\n if (this.config.warmup === 'face') img = atob(sample.face);\n if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n if (!img) return null;\n let res;\n if (typeof tf['node'] !== 'undefined') {\n const data = tf['node'].decodeJpeg(img);\n const expanded = data.expandDims(0);\n this.tf.dispose(data);\n // log('Input:', expanded);\n res = await this.detect(expanded, this.config);\n this.tf.dispose(expanded);\n } else {\n if (this.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await this.detect(input, this.config);\n */\n }\n return res;\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n */\n async warmup(userConfig?: Config | Record): Promise {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\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 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\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"], + "mappings": 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r=D(e,"labels","hingeLoss"),a=D(t,"predictions","hingeLoss"),o=null;n!=null&&(o=D(n,"weights","hingeLoss")),fn(r.shape,a.shape,"Error in hingeLoss: ");let i=Ie(1);r=Ae(z(Ie(2),r),i);let l=Ys(Ae(i,z(r,a)));return yr(l,o,s)}var $_=V({hingeLoss_:__});function F_(e,t,n,s=1,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","huberLoss"),o=D(t,"predictions","huberLoss"),i=null;n!=null&&(i=D(n,"weights","huberLoss")),fn(a.shape,o.shape,"Error in huberLoss: ");let l=Ie(s),u=Wt(Ae(o,a)),c=oc(u,l),d=Ae(u,c),h=ae(z(Ie(.5),lt(c)),z(l,d));return yr(h,i,r)}var D_=V({huberLoss_:F_});function O_(e,t,n,s=1e-7,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","logLoss"),o=D(t,"predictions","logLoss"),i=null;n!=null&&(i=D(n,"weights","logLoss")),fn(a.shape,o.shape,"Error in logLoss: ");let l=Ie(1),u=Ie(s),c=St(z(a,is(ae(o,u)))),d=z(Ae(l,a),is(ae(Ae(l,o),u))),h=Ae(c,d);return yr(h,i,r)}var P_=V({logLoss_:O_});function M_(e,t,n,s=yn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","meanSquaredError"),a=D(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=D(n,"weights","meanSquaredError")),fn(r.shape,a.shape,"Error in meanSquaredError: ");let i=MA(r,a);return yr(i,o,s)}var z_=V({meanSquaredError_:M_});function L_(e,t){let n=D(e,"labels","sigmoidCrossEntropyWithLogits"),s=D(t,"logits","sigmoidCrossEntropyWithLogits");fn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ys(s),a=z(s,n),o=Bh(os(St(Wt(s))));return ae(Ae(r,a),o)}function B_(e,t,n,s=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"multiClassLabels","sigmoidCrossEntropy"),o=D(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","sigmoidCrossEntropy")),fn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let u=Ie(s),c=Ie(1),d=Ie(.5);a=ae(z(a,Ae(c,u)),z(d,u))}let l=L_(a,o);return yr(l,i,r)}var W_=V({sigmoidCrossEntropy_:B_});function V_(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 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Labels / logits was rank ${t.rank} and dim was ${n}`);return Zs((r,a,o)=>{let l=Jx(a,[n],!0),u=Ae(ue(a,"float32"),l);o([r,u]);let c=St(z(u,r));return{value:ve(c,[n]),gradFunc:(p,m)=>{let[f,A]=m,g=Co(p.shape,[n]);return[z(U(p,g),Ae(ue(f,"float32"),os(A))),z(U(p,g),Ae(os(A),ue(f,"float32")))]}}})(e,t)}function U_(e,t,n,s=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"onehotLabels","softmaxCrossEntropy"),o=D(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","softmaxCrossEntropy")),fn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let u=Ie(s),c=Ie(1),d=Ie(a.shape[1]);a=ae(z(a,Ae(c,u)),ce(u,d))}let l=V_(a,o);return yr(l,i,r)}var H_=V({softmaxCrossEntropy_:U_});function G_(e,t,n,s){let r=D(e,"indices","sparseFillEmptyRows"),a=D(t,"values","sparseFillEmptyRows"),o=D(n,"denseShape","sparseFillEmptyRows"),i=D(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape - ${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},u=L.runKernel(dh,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var j_=V({sparseFillEmptyRows_:G_});function q_(e,t,n){let s=D(e,"inputIndices","sparseReshape"),r=D(t,"inputShape","sparseReshape"),a=D(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape - ${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=L.runKernel(hh,o);return{outputIndices:i[0],outputShape:i[1]}}var X_=V({sparseReshape_:q_});function K_(e,t,n){let s=D(e,"data","sparseSegmentMean"),r=D(t,"indices","sparseSegmentMean"),a=D(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape - ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${a.shape}`);let o={data:s,indices:r,segmentIds:a};return L.runKernel(ph,o)}var Z_=V({sparseSegmentMean_:K_});function Y_(e,t,n){let s=D(e,"data","sparseSegmentSum"),r=D(t,"indices","sparseSegmentSum"),a=D(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape - ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${a.shape}`);let o={data:s,indices:r,segmentIds:a};return 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n$=V({stringSplit_:t$});function s$(e,t){let n=D(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(yh,r,s)}var r$=V({stringToHashBucketFast_:s$}),a$={fft:Yh,ifft:uc,rfft:Jh,irfft:PA},o$={hammingWindow:PR,hannWindow:vb,frame:wb,stft:BR},Re={flipLeftRight:HR,resizeNearestNeighbor:f_,resizeBilinear:h_,rotateWithOffset:jR,cropAndResize:VR,nonMaxSuppression:XR,nonMaxSuppressionAsync:n_,nonMaxSuppressionWithScore:r_,nonMaxSuppressionWithScoreAsync:o_,nonMaxSuppressionPadded:l_,nonMaxSuppressionPaddedAsync:c_,threshold:g_,transform:x_},Nb={bandPart:v_,gramSchmidt:k_,qr:S_},i$={absoluteDifference:N_,computeWeightedLoss:yr,cosineDistance:R_,hingeLoss:$_,huberLoss:D_,logLoss:P_,meanSquaredError:z_,sigmoidCrossEntropy:W_,softmaxCrossEntropy:H_},dc={sparseFillEmptyRows:j_,sparseReshape:X_,sparseSegmentMean:Z_,sparseSegmentSum:J_},rp={stringNGrams:e$,stringSplit:n$,stringToHashBucketFast:r$},xr=class extends px{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return Z(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Xx(e,t)}dispose(){this.iterations_!=null&&Z(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(xr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ap=class extends xr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>qe(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>qe(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;H(()=>{let u=ae(z(i,this.rho),z(lt(o),1-this.rho)),c=z(ce(ln(ae(l,this.epsilon)),ln(ae(i,this.epsilon))),o),d=ae(z(l,this.rho),z(lt(c),1-this.rho));i.assign(u),l.assign(d);let h=ae(z(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Z(this.accumulatedGrads.map(e=>e.variable)),Z(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ap.className="Adadelta";Vr(ap);var op=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>Sl(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;H(()=>{let i=ae(o,lt(a));o.assign(i);let l=ae(z(ce(a,ln(ae(i,L.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Z(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)}};op.className="Adagrad";Vr(op);var ip=class extends xr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=Ae(1,this.accBeta1),s=Ae(1,this.accBeta2);t.forEach((r,a)=>{let o=L.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:H(()=>qe(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:H(()=>qe(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=ae(z(u,this.beta1),z(l,1-this.beta1)),h=ae(z(c,this.beta2),z(lt(l),1-this.beta2)),p=ce(d,n),m=ce(h,s);u.assign(d),c.assign(h);let f=ae(z(ce(p,ae(ln(m),this.epsilon)),-this.learningRate),o);o.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Z(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Z(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),H(()=>{this.accBeta1.assign(jr(this.beta1,this.iterations_+1)),this.accBeta2.assign(jr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};ip.className="Adam";Vr(ip);var lp=class extends xr{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=Ae(1,this.accBeta1),s=ce(-this.learningRate,ae(z(this.iteration,this.decay),1));t.forEach((r,a)=>{let 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For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new wr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Fs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Fs("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 Fs("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 Fs("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={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else I.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."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Ml))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Ms(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("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 G("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}}};Ml.className="Sequential";oe.registerClass(Ml);function zP(e){return new wr(e)}function LP(e){return new Ml(e)}function BP(e,t){return t==null&&(t={}),OP(e,t)}function L3(e){return d3(e)}function WP(e,t){ks.registerCallbackConstructor(e,t)}var _n=class extends oe.Serializable{getConfig(){return{}}},B3=class extends _n{apply(e,t=1){return pO(e,t)}};B3.className="elu";oe.registerClass(B3);var W3=class extends _n{apply(e){return $A(e)}};W3.className="selu";oe.registerClass(W3);var V3=class extends _n{apply(e){return Ys(e)}};V3.className="relu";oe.registerClass(V3);var U3=class extends _n{apply(e){return H(()=>oc(6,Ys(e)))}};U3.className="relu6";oe.registerClass(U3);var H3=class extends _n{apply(e){return e}};H3.className="linear";oe.registerClass(H3);var G3=class extends _n{apply(e){return Bn(e)}};G3.className="sigmoid";oe.registerClass(G3);var j3=class extends _n{apply(e){return mO(e)}};j3.className="hardSigmoid";oe.registerClass(j3);var q3=class extends _n{apply(e){return Tl(e)}};q3.className="softplus";oe.registerClass(q3);var X3=class extends _n{apply(e){return fO(e)}};X3.className="softsign";oe.registerClass(X3);var K3=class extends _n{apply(e){return wl(e)}};K3.className="tanh";oe.registerClass(K3);var Eg=class extends _n{apply(e,t=-1){return Zh(e,t)}};Eg.className="softmax";oe.registerClass(Eg);var Z3=class extends _n{apply(e,t=-1){return vA(e,t)}};Z3.className="logSoftmax";oe.registerClass(Z3);var Y3=class extends _n{apply(e,t=1){return H(()=>z(Bn(z(e,t)),e))}};Y3.className="swish";oe.registerClass(Y3);var J3=class extends _n{apply(e){return H(()=>z(e,wl(Tl(e))))}};J3.className="mish";oe.registerClass(J3);function Jr(e){return e.getClassName()}function Rg(e,t={}){return pc(e,oe.SerializationMap.getMap().classNameMap,t,"activation")}function Qr(e){if(e==null){let t={};return t.className="linear",t.config={},Rg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Rg(t)}else return e instanceof _n?e:Rg(e)}function _g(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 Q3=class extends oe.Serializable{},kc=class extends Q3{constructor(e){super();_g(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 H(()=>{let t=Dt([1]);return this.hasL1&&(t=ae(t,ve(z(this.l1,Wt(e))))),this.hasL2&&(t=ae(t,ve(z(this.l2,gc(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};kc.className="L1L2";oe.registerClass(kc);function VP(e){return _g(e),new kc({l1:e!=null?e.l1:null,l2:0})}function UP(e){return _g(e),new kc({l2:e!=null?e.l2:null,l1:0})}var ev={l1l2:"L1L2"};function ut(e){return XA(e)}function tv(e,t={}){return pc(e,oe.SerializationMap.getMap().classNameMap,t,"regularizer")}function vt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in ev?ev[e]:e,config:{}};return tv(n)}else return e instanceof Q3?e:tv(e)}var $g=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Ys(e);return this.maxValue!=null&&(n=Wn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};$g.className="ReLU";oe.registerClass($g);var Fg=class extends Xe{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=ze(e);return Lh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Fg.className="LeakyReLU";oe.registerClass(Fg);var Dg=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=bt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=vt(e.alphaRegularizer),this.alphaConstraint=Gt(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 G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s($t(t),t==="channelsFirst"?je(e,[0,2,3,1]):e))}function nv(e,t){return H(()=>($t(t),t==="channelsFirst"?je(e,[0,2,3,4,1]):e))}function HP(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=$s()),$t(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=je(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=pA(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Os(i,n)),i})}function sv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=$s()),$t(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=zg(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=qr.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=je(l,[0,3,1,2])),l})}function GP(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=$s()),$t(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=nv(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=AA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Os(i,n)),a==="channelsFirst"&&(i=je(i,[0,4,1,2,3])),i})}var Lg=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Lg.verifyArgs(t),this.rank=e,Zt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zl(t.kernelSize,e,"kernelSize"),this.strides=zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,hs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=Qr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=bt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Gt(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`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 G(`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 G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ZA(e.kernelSize,"number",1,3))throw new G(`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:Jr(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Ht(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ic=class extends Lg{constructor(e,t){super(e,t);this.kernel=null,Ic.verifyArgs(t),this.filters=t.filters,Zt(this.filters,"filters"),this.kernelInitializer=bt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Gt(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 H(()=>{e=ze(e);let n,s=this.bias==null?null:this.bias.read(),r=jb(this.activation.getClassName());if(r!=null&&this.rank===2)n=sv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=HP(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=sv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=GP(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Sc=class extends Ic{constructor(e){super(2,e);Sc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ZA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sc.className="Conv2D";oe.registerClass(Sc);var Cc=class extends Ic{constructor(e){super(3,e);Cc.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 G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Cc.className="Conv3D";oe.registerClass(Cc);var Bg=class extends Sc{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],h=this.strides[1],p=nr(i,d,u,this.padding),m=nr(l,h,c,this.padding),f=[r,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=je(n,[0,2,3,1]));let A=mA(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=je(A,[0,3,1,2])),this.bias!=null&&(A=Os(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=st(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=nr(t[s],i,a,this.padding),t[r]=nr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Bg.className="Conv2DTranspose";oe.registerClass(Bg);var Wg=class extends Cc{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],d=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],g=nr(l,m,d,this.padding),y=nr(u,f,h,this.padding),x=nr(c,A,p,this.padding),b=[r,g,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=je(n,[0,2,3,4,1]));let v=Mx(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=je(v,[0,4,1,2,3])),this.bias!==null&&(v=Os(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=st(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=nr(t[s],u,o,this.padding),t[r]=nr(t[r],c,i,this.padding),t[a]=nr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Wg.className="Conv3DTranspose";oe.registerClass(Wg);var rv=class extends Ic{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 G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("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 G(`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=bt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Gt(t.depthwiseConstraint),this.pointwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Gt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length{e=ze(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=je(e,[0,2,3,1])),n=ab(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Os(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=je(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Ht(this.depthwiseConstraint),e.pointwiseConstraint=Ht(this.pointwiseConstraint),e}};rv.className="SeparableConv";var Vg=class extends rv{constructor(e){super(2,e)}};Vg.className="SeparableConv2D";oe.registerClass(Vg);var Pp=class extends Ic{constructor(e){super(1,e);Pp.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"&&!ZA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Pp.className="Conv1D";oe.registerClass(Pp);var Ug=class extends Xe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=fp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return fp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=fp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return fp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ug.className="Cropping2D";oe.registerClass(Ug);var Hg=class extends Xe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,oO(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 H(()=>{let n=ze(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=je(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Re.resizeNearestNeighbor(n,[r,a]):Re.resizeBilinear(n,[r,a]);return je(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Re.resizeNearestNeighbor(n,[r,a]):Re.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hg.className="UpSampling2D";oe.registerClass(Hg);function jP(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=$s()),$t(r);let o=zg(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=sc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}var Gg=class extends Lg{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=bt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Gt(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 H(()=>{e=ze(e);let n=jP(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Os(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=zs(t,this.kernelSize[0],this.padding,this.strides[0]),a=zs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Ht(this.depthwiseRegularizer),e}};Gg.className="DepthwiseConv2D";oe.registerClass(Gg);function av(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ov(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ds(2,l));if(t=je(t,u),a!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ue(ue(r,"bool"),"float32"),r.rank===l-1&&(r=Ft(r,-1)),r=je(r,u)),s&&(t=cs(t,0),r!=null&&(r=cs(r,0)));let c=[],d,h=n,p=t.shape[0],m=ds(t),f;r!=null&&(f=ds(r));for(let g=0;ge(y,h));if(r==null)d=x[0],h=x[1];else{let b=H(()=>{let v=f[g],k=Ae(us(v),v),w=ae(z(x[0],v),z(h[0],k)),C=h.map((E,P)=>ae(z(x[1][P],v),z(E,k)));return{output:w,newStates:C}});d=b.output,h=b.newStates}i&&c.push(d)}let A;return i&&(A=Nn(c,1)),[d,A,h]})}var sr=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Lp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("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 Pt({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 Ds(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){pg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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;no.shape[o.shape.length-1]),a))throw new G(`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=a.map(o=>new Pt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("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(s=>Dt([n,s])):this.states_=[Dt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Dt([n,s])):this.states_[0]=Dt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`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()):Z(this.states_);for(let s=0;sKt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=av(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Pt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Ps){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=ze(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=ov((p,m)=>{let f=this.cell.call([p].concat(m),o);return[f[0],f.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?c:u;return this.returnState?[h].concat(d):h})}getInitialState(e){return H(()=>{let t=Dt(e.shape);return t=ve(t,[1,2]),t=Ac(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?rg(t,[1,n]):t):this.cell.stateSize>1?[rg(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()===sr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Ms(s,n);return new e(Object.assign(t,{cell:r}))}};sr.className="RNN";oe.registerClass(sr);var Tc=class extends Xe{},Mp=class extends Tc{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,Zt(this.units,"units"),this.activation=Qr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0us(e),rate:this.dropout,training:s})),0us(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=er(z(e,a),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=Os(r,this.bias.read())),o!=null&&(n=z(n,o));let i=ae(r,er(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Jr(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),recurrentConstraint:Ht(this.recurrentConstraint),biasConstraint:Ht(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Mp.className="SimpleRNNCell";oe.registerClass(Mp);var jg=class extends sr{constructor(e){e.cell=new Mp(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};jg.className="SimpleRNN";oe.registerClass(jg);var zp=class extends Tc{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 G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Zt(this.units,"units"),this.activation=Qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0us(e),rate:this.dropout,training:n,count:3})),0us(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qg.className="GRU";oe.registerClass(qg);var Nc=class extends Tc{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,Zt(this.units,"units"),this.activation=Qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([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=st(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ws{apply(i,l){let u=r.apply([a]),c=new Ap().apply([a]),d=r.apply([a*2]);return t3(t3(u,c),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0us(e),rate:this.dropout,training:n,count:4})),0us(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Xg.className="LSTM";oe.registerClass(Xg);var Lp=class extends Tc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{$o(`RNNCell_${s}`,()=>{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()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Ms(r,n));return new e({cells:s})}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 fg(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;as3(t(),n),o=()=>yc(a,t,s);return!r||r<=1?Kt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Kt(l.clone()))}var qP=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}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 H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Dt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("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(()=>Dt(r)):this.states_=[Dt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Dt(r)):this.states_[0]=Dt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let o=0;oKt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=zs(l,s[0],r,a[0],o[0]),d=zs(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};iv.className="ConvRNN2D";var Bp=class extends Nc{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,Zt(this.filters,"filters"),this.kernelSize=zl(n,2,"kernelSize"),this.kernelSize.forEach(i=>Zt(i,"kernelSize")),this.strides=zl(s||1,2,"strides"),this.strides.forEach(i=>Zt(i,"strides")),this.padding=r||"valid",hs(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=zl(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Zt(i,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends ws{apply(d,h){let p=l.apply([u]),m=Un([u]),f=l.apply([u*2]);return sg([p,m,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0us(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(Q,ne,te)=>!ne||!ne[te]?Q:z(ne[te],Q),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),h=l(s,i,3);0us(r),rate:this.recurrentDropout,training:n,count:o}));let p=this.recurrentDropoutMask,m=l(r,p,0),f=l(r,p,1),A=l(r,p,2),g=l(r,p,3),y=3,[x,b,v,k]=nn(this.kernel.read(),o,y),[w,C,E,P]=this.useBias?nn(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,w,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,E,this.padding),h=this.inputConv(h,k,P,this.padding);let[R,_,T,O]=nn(this.recurrentKernel.read(),o,y);m=this.recurrentConv(m,R),f=this.recurrentConv(f,_),A=this.recurrentConv(A,T),g=this.recurrentConv(g,O);let W=this.recurrentActivation.apply(ae(u,m)),j=this.recurrentActivation.apply(ae(c,f)),q=ae(z(j,a),z(W,this.activation.apply(ae(d,A)))),X=z(this.recurrentActivation.apply(ae(h,g)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=qP(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Hr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Os(r,n,this.dataFormat):r}recurrentConv(e,t){return Hr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Bp.className="ConvLSTM2DCell";oe.registerClass(Bp);var Kg=class extends iv{constructor(e){let t=new Bp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Kg.className="ConvLSTM2D";oe.registerClass(Kg);var Wp=class extends Xe{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 s=0;s{this.invokeCallHook(e,t);let n=ze(e);if(0s3(n,this.rate,r,this.seed),()=>n,s)}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";oe.registerClass(Wp);var Zg=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Zg.className="SpatialDropout1D";oe.registerClass(Zg);var Yg=class extends Xe{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,Zt(this.units,"units"),this.activation=Qr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Gt(e.kernelConstraint),this.biasConstraint=Gt(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=jb(this.activation.getClassName()),r;return s!=null?r=er(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=Os(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Jr(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),biasConstraint:Ht(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Yg.className="Dense";oe.registerClass(Yg);var Jg=class extends Xe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new G(`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],Kr(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:Jr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Qg.className="Activation";oe.registerClass(Qg);var e2=class extends Xe{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 H(()=>(e=ze(e),cO(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};e2.className="RepeatVector";oe.registerClass(e2);var t2=class extends Xe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=ze(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};t2.className="Reshape";oe.registerClass(t2);var n2=class extends Xe{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=Ds(1,e.dims.length+1);if(!I.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 Pt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return je(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};n2.className="Permute";oe.registerClass(n2);var s2=class extends Xe{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=ze(e),s=-1;return Fh(Nl(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=-1,r=!0,a=Fh(Nl(n,this.maskValue),s,r);return z(n,ue(a,n.dtype))})}};s2.className="Masking";oe.registerClass(s2);var r2=class extends Xe{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(At(e.inputLength))}this.inputDim=e.inputDim,Zt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Zt(this.outputDim,"outputDim"),this.embeddingsInitializer=bt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Gt(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 H(()=>this.maskZero?(e=ze(e),Nl(e,qe(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=At(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s{this.invokeCallHook(e,t);let n=ze(e);n.dtype!=="int32"&&(n=pp(n,"int32"));let s=n3(this.embeddings.read(),U(n,[n.size]));return U(s,st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Ht(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};r2.className="Embedding";oe.registerClass(r2);var Mo=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Oe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&Xr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return H(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Zr(s);for(let a of e){let o=a.rank;for(let i=0;i1){let u=Ds(1,l).concat([0]);n.push(je(i,u)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=U(je(U(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(Ds(0,o-1));a=je(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ft(s,0));let n=t[0];for(let s=1;s{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),I.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 Oe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;us){o=r-s;let l=[];for(let u=0;u0){let l;s>r?l=s+r-3:l=s-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 Oe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Ec(r,e[a].shape.length)):s=[Ec(this.axes,t.shape.length),Ec(this.axes,n.shape.length)],this.normalize&&(t=Np(t,s[0]),n=Np(n,s[1])),XP(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ec(this.axes,e.length),Ec(this.axes,t.length)],n}computeOutputShape(e){I.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 Oe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};d2.className="Dot";oe.registerClass(d2);var h2=class extends Xe{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 H(()=>{this.invokeCallHook(e,t);let n=ze(e);return yc(()=>ae(mp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};h2.className="GaussianNoise";oe.registerClass(h2);var p2=class extends Xe{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 H(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?yc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return z(n,mp(n.shape,1,r))},()=>n,t.training||!1):n})}};p2.className="GaussianDropout";oe.registerClass(p2);var f2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(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 H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return yc(()=>{let r=ze(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Io(El(n),this.rate);l=pp(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ae(z(r,l),z(ae(l,-1),i));return ae(z(d,u),c)},()=>ze(e),t.training||!1)}return e})}};f2.className="AlphaDropout";oe.registerClass(f2);function Rc(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=Ex(e,t,n,s,r,a);else if(e.rank===3)o=Rx(e,t,n,s,r,a);else if(e.rank===4)o=_x(e,t,n,s,r,a);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function KP(e,t,n,s,r=.001){return H(()=>{let a=Gh(e,s),o=a.mean,i=a.variance;return[Rc(e,o,i,n,t,r),o,i]})}function ZP(e,t,n,s,r=.001){return H(()=>{let a=Gh(e,s),o=a.mean,i=a.variance,l=[];for(let m of Ds(0,e.rank))s.indexOf(m)!==-1?l.push(1):l.push(e.shape[m]);let u=U(o,l),c=U(i,l),d=t==null?null:U(t,l),h=n==null?null:U(n,l);return[Rc(e,u,c,h,d,r),o,i]})}function YP(e,t,n,s,r=.001){return I.arraysEqual(s.slice().sort(),Ds(0,e.rank-1))?KP(e,t,n,s,r):ZP(e,t,n,s,r)}var m2=class extends Xe{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=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.movingMeanInitializer=bt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=bt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Gt(e.betaConstraint),this.gammaConstraint=Gt(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Pt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=ze(e),r=s.shape,a=r.length,o=Ds(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Eo(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!I.arraysEqual(u,Ds(0,a).slice(0,a-1)),d=()=>{if(c){let g=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),x=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return Rc(s,g,y,x,b,this.epsilon)}else return Rc(s,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 d();let[h,p,m]=YP(s,this.gamma.read(),this.beta.read(),o,this.epsilon),f=(g,y,x)=>{H(()=>{let b=1-x,v=g.read(),k=z(Ae(v,y),b);g.write(Ae(v,k))})};return(()=>{f(this.movingMean,p,this.momentum),f(this.movingVariance,m,this.momentum)})(),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Ht(this.betaConstraint),gammaConstraint:Ht(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};m2.className="BatchNormalization";oe.registerClass(m2);var A2=class extends Xe{constructor(e){e==null&&(e={});super(e);if(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=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Xr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=ze(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=Gh(n,this.axis,a),l=Eo(1,r);for(let m of this.axis)l[m]=s[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?U(m,l):m,c=u(this.gamma.read()),d=u(this.beta.read()),h=[],p=[];for(let m=0;m{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=$s()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Gr(e,s)})}var g2=class extends Xe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?$s():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 G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a 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a==="max"?o=Uh(e,t,n,i):o=Oh(e,t,n,i),r==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}function lv(e,t,n,s,r,a){return H(()=>{$t(r),Zb(a),hs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=$s()),a==null&&(a="max"),e=nv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=SA(e,t,n,i):o=dA(e,t,n,i),r==="channelsFirst"&&(o=je(o,[0,4,1,2,3])),o})}var uv=class extends Xe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Zt(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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t=zs(t,this.poolSize[0],this.padding,this.strides[0]),n=zs(n,this.poolSize[1],this.padding,this.strides[1]),s=zs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},w2=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),lv(e,t,n,s,r,"max")}};w2.className="MaxPooling3D";oe.registerClass(w2);var k2=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),lv(e,t,n,s,r,"avg")}};k2.className="AveragePooling3D";oe.registerClass(k2);var hv=class extends Xe{constructor(e){super(e);this.inputSpec=[new Pt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, - because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Is(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been 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return require(x); + throw new Error('Dynamic require of "' + x + '" is not supported'); +}; +var __export = (target, all6) => { + __markAsModule(target); + for (var name in all6) + __defProp(target, name, { get: all6[name], enumerable: true }); +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// src/helpers.ts +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`); + return path; +} +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "webgl", + modelBasePath: "../models/", + wasmPath: "../node_modules/@tensorflow/tfjs-backend-wasm/dist/", + debug: true, + async: true, + warmup: "full", + cacheSensitivity: 0.75, + skipFrame: false, + filter: { + enabled: true, + width: 0, + height: 0, + flip: false, + return: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: true, + maxDetected: 15, + skipFrames: 15, + minConfidence: 0.2, + iouThreshold: 0.1, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 11, + minConfidence: 0.1 + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 17, + modelPath: "emotion.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: 1, + minConfidence: 0.2, + skipFrames: 1 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 18, + minConfidence: 0.1, + iouThreshold: 0.1, + maxDetected: 2, + landmarks: true, + detector: { + modelPath: "handdetect.json" + }, + skeleton: { + modelPath: "handskeleton.json" + } + }, + object: { + enabled: false, + modelPath: "mb3-centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 19 + }, + segmentation: { + enabled: false, + modelPath: "selfie.json" + } +}; + +// src/sysinfo.ts +function info() { + let platform; + let agent; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw && raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + platform = platformMatch ? platformMatch[0].replace(/\(|\)/g, "") : ""; + agent = navigator.userAgent.replace(raw[0], ""); + if (platform[1]) + agent = agent.replace(raw[1], ""); + agent = agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + platform = `${process.platform} ${process.arch}`; + agent = `NodeJS ${process.version}`; + } + return { platform, agent }; +} + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + Abs: () => Abs, + Acos: () => Acos, + Acosh: () => Acosh, + AdadeltaOptimizer: () => AdadeltaOptimizer, + AdagradOptimizer: () => AdagradOptimizer, + AdamOptimizer: () => AdamOptimizer, + AdamaxOptimizer: () => AdamaxOptimizer, + Add: () => Add, + AddN: () => AddN, + All: () => All, + Any: () => Any, + ArgMax: () => ArgMax, + ArgMin: () => ArgMin, + Asin: () => Asin, + Asinh: () => Asinh, + Atan: () => Atan, + Atan2: () => Atan2, + Atanh: () => Atanh, + AvgPool: () => AvgPool, + AvgPool3D: () => AvgPool3D, + AvgPool3DGrad: () => AvgPool3DGrad, + AvgPoolGrad: () => AvgPoolGrad, + BackendWasm: () => BackendWasm, + BatchMatMul: () => BatchMatMul, + BatchToSpaceND: () => BatchToSpaceND, + Bincount: () => Bincount, + BroadcastTo: () => BroadcastTo, + Callback: () => Callback, + CallbackList: () => CallbackList, + Cast: () => Cast, + Ceil: () => Ceil, + ClipByValue: () => ClipByValue, + Complex: () => Complex, + ComplexAbs: () => ComplexAbs, + Concat: () => Concat, + Conv2D: () => Conv2D, + Conv2DBackpropFilter: () => Conv2DBackpropFilter, + Conv2DBackpropInput: () => Conv2DBackpropInput, + Conv3D: () => Conv3D, + Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2, + Conv3DBackpropInputV2: () => Conv3DBackpropInputV2, + Cos: () => Cos, + Cosh: () => Cosh, + CropAndResize: () => CropAndResize, + Cumsum: () => Cumsum, + CustomCallback: () => CustomCallback, + DataStorage: () => DataStorage, + DenseBincount: () => DenseBincount, + DepthToSpace: () => DepthToSpace, + DepthwiseConv2dNative: () => DepthwiseConv2dNative, + DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter, + DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput, + Diag: () => Diag, + Dilation2D: () => Dilation2D, + Dilation2DBackpropFilter: () => Dilation2DBackpropFilter, + Dilation2DBackpropInput: () => Dilation2DBackpropInput, + ENV: () => ENV, + EarlyStopping: () => EarlyStopping, + Einsum: () => Einsum, + Elu: () => Elu, + EluGrad: () => EluGrad, + Environment: () => Environment, + Equal: () => Equal, + Erf: () => Erf, + Exp: () => Exp, + ExpandDims: () => ExpandDims, + Expm1: () => Expm1, + FFT: () => FFT, + Fill: () => Fill, + FlipLeftRight: () => FlipLeftRight, + Floor: () => Floor, + FloorDiv: () => FloorDiv, + FromPixels: () => FromPixels, + FusedBatchNorm: () => FusedBatchNorm, + FusedConv2D: () => FusedConv2D, + FusedDepthwiseConv2D: () => FusedDepthwiseConv2D, + GPGPUContext: () => GPGPUContext, + GatherNd: () => GatherNd, + GatherV2: () => GatherV2, + GraphModel: () => GraphModel, + Greater: () => Greater, + GreaterEqual: () => GreaterEqual, + History: () => History, + IFFT: () => IFFT, + Identity: () => Identity, + Imag: () => Imag, + InputSpec: () => InputSpec, + IsFinite: () => IsFinite, + IsInf: () => IsInf, + IsNan: () => IsNan, + KernelBackend: () => KernelBackend, + LRN: () => LRN, + LRNGrad: () => LRNGrad, + LayerVariable: () => LayerVariable, + LayersModel: () => LayersModel, + LeakyRelu: () => LeakyRelu, + Less: () => Less, + LessEqual: () => LessEqual, + LinSpace: () => LinSpace, + Log: () => Log, + Log1p: () => Log1p, + LogSoftmax: () => LogSoftmax, + LogicalAnd: () => LogicalAnd, + LogicalNot: () => LogicalNot, + LogicalOr: () => LogicalOr, + MathBackendCPU: () => MathBackendCPU, + MathBackendWebGL: () => MathBackendWebGL, + Max: () => Max, + MaxPool: () => MaxPool, + MaxPool3D: () => MaxPool3D, + MaxPool3DGrad: () => MaxPool3DGrad, + MaxPoolGrad: () => MaxPoolGrad, + MaxPoolWithArgmax: () => MaxPoolWithArgmax, + Maximum: () => Maximum, + Mean: () => Mean, + Min: () => Min, + Minimum: () => Minimum, + MirrorPad: () => MirrorPad, + Mod: () => Mod, + MomentumOptimizer: () => MomentumOptimizer, + Multinomial: () => Multinomial, + Multiply: () => Multiply, + Neg: () => Neg, + NonMaxSuppressionV3: () => NonMaxSuppressionV3, + NonMaxSuppressionV4: () => NonMaxSuppressionV4, + NonMaxSuppressionV5: () => NonMaxSuppressionV5, + NotEqual: () => NotEqual, + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + OneHot: () => OneHot, + OnesLike: () => OnesLike, + Optimizer: () => Optimizer, + Pack: () => Pack, + PadV2: () => PadV2, + Pool: () => Pool, + Pow: () => Pow, + Prelu: () => Prelu, + Prod: () => Prod, + RMSPropOptimizer: () => RMSPropOptimizer, + RNN: () => RNN, + Range: () => Range, + Rank: () => Rank, + Real: () => Real, + RealDiv: () => RealDiv, + Reciprocal: () => Reciprocal, + Reduction: () => Reduction, + Relu: () => Relu, + Relu6: () => Relu6, + Reshape: () => Reshape, + ResizeBilinear: () => ResizeBilinear, + ResizeBilinearGrad: () => ResizeBilinearGrad, + ResizeNearestNeighbor: () => ResizeNearestNeighbor, + ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad, + Reverse: () => Reverse, + RotateWithOffset: () => RotateWithOffset, + Round: () => Round, + Rsqrt: () => Rsqrt, + SGDOptimizer: () => SGDOptimizer, + ScatterNd: () => ScatterNd, + Select: () => Select, + Selu: () => Selu, + Sequential: () => Sequential, + Sigmoid: () => Sigmoid, + Sign: () => Sign, + Sin: () => Sin, + Sinh: () => Sinh, + Slice: () => Slice, + Softmax: () => Softmax, + Softplus: () => Softplus, + SpaceToBatchND: () => SpaceToBatchND, + SparseFillEmptyRows: () => SparseFillEmptyRows, + SparseReshape: () => SparseReshape, + SparseSegmentMean: () => SparseSegmentMean, + SparseSegmentSum: () => SparseSegmentSum, + SparseToDense: () => SparseToDense, + SplitV: () => SplitV, + Sqrt: () => Sqrt, + Square: () => Square, + SquaredDifference: () => SquaredDifference, + Step: () => Step, + StridedSlice: () => StridedSlice, + StringNGrams: () => StringNGrams, + StringSplit: () => StringSplit, + StringToHashBucketFast: () => StringToHashBucketFast, + Sub: () => Sub, + Sum: () => Sum, + SymbolicTensor: () => SymbolicTensor, + Tan: () => Tan, + Tanh: () => Tanh, + Tensor: () => Tensor, + TensorBuffer: () => TensorBuffer, + Tile: () => Tile, + TopK: () => TopK, + Transform: () => Transform, + Transpose: () => Transpose, + Unique: () => Unique, + Unpack: () => Unpack, + UnsortedSegmentSum: () => UnsortedSegmentSum, + Variable: () => Variable, + ZerosLike: () => ZerosLike, + _FusedMatMul: () => _FusedMatMul, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + backend: () => backend, + backend_util: () => backend_util_exports, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + booleanMaskAsync: () => booleanMaskAsync, + broadcastTo: () => broadcastTo, + browser: () => browser_exports, + buffer: () => buffer, + callbacks: () => callbacks, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + constraints: () => exports_constraints_exports, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + copyRegisteredKernels: () => copyRegisteredKernels, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumsum: () => cumsum, + customGrad: () => customGrad, + data: () => dist_exports, + denseBincount: () => denseBincount, + deprecationWarn: () => deprecationWarn, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + deregisterOp: () => deregisterOp, + device_util: () => device_util_exports, + diag: () => diag, + dilation2d: () => dilation2d, + disableDeprecationWarnings: () => disableDeprecationWarnings, + dispose: () => dispose, + disposeVariables: () => disposeVariables, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + einsum: () => einsum, + elu: () => elu, + enableDebugMode: () => enableDebugMode, + enableProdMode: () => enableProdMode, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + engine: () => engine, + env: () => env, + equal: () => equal, + erf: () => erf, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + findBackend: () => findBackend, + findBackendFactory: () => findBackendFactory, + floor: () => floor, + floorDiv: () => floorDiv, + forceHalfFloat: () => forceHalfFloat, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + gather_util: () => gather_nd_util_exports, + getBackend: () => getBackend, + getGradient: () => getGradient, + getKernel: () => getKernel, + getKernelsForBackend: () => getKernelsForBackend, + gpgpu_util: () => gpgpu_util_exports, + grad: () => grad, + grads: () => grads, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + initializers: () => exports_initializers_exports, + input: () => input, + io: () => io_exports, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + keep: () => keep, + kernel_impls: () => kernel_impls_exports, + layers: () => exports_layers_exports, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + loadGraphModel: () => loadGraphModel, + loadLayersModel: () => loadLayersModel, + localResponseNormalization: () => localResponseNormalization, + log: () => log2, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + matMul: () => matMul, + math: () => math_exports, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + memory: () => memory, + meshgrid: () => meshgrid, + metrics: () => exports_metrics_exports, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + model: () => model, + models: () => exports_models_exports, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + nextFrame: () => nextFrame, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print2, + prod: () => prod, + profile: () => profile, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomUniform: () => randomUniform, + range: () => range, + ready: () => ready, + real: () => real, + reciprocal: () => reciprocal, + registerBackend: () => registerBackend, + registerCallbackConstructor: () => registerCallbackConstructor, + registerGradient: () => registerGradient, + registerKernel: () => registerKernel, + registerOp: () => registerOp, + regularizers: () => exports_regularizers_exports, + relu: () => relu, + relu6: () => relu6, + removeBackend: () => removeBackend, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + scatter_util: () => scatter_nd_util_exports, + selu: () => selu, + separableConv2d: () => separableConv2d, + sequential: () => sequential, + serialization: () => serialization_exports, + setBackend: () => setBackend, + setPlatform: () => setPlatform, + setWasmPath: () => setWasmPath, + setWasmPaths: () => setWasmPaths, + setWebGLContext: () => setWebGLContext, + setdiff1dAsync: () => setdiff1dAsync, + shared: () => shared_exports, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + slice_util: () => slice_util_exports, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparse: () => sparse, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + string: () => string, + sub: () => sub, + sum: () => sum2, + sumOutType: () => sumOutType, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensor_util: () => tensor_util_exports, + test_util: () => test_util_exports, + tidy: () => tidy, + tile: () => tile, + time: () => time, + topk: () => topk, + train: () => train, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unregisterGradient: () => unregisterGradient, + unregisterKernel: () => unregisterKernel, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upcastType: () => upcastType, + util: () => util_exports, + valueAndGrad: () => valueAndGrad, + valueAndGrads: () => valueAndGrads, + variable: () => variable, + variableGrads: () => variableGrads, + version: () => version16, + version_converter: () => version11, + version_core: () => version9, + version_cpu: () => version13, + version_layers: () => version10, + version_wasm: () => version15, + version_webgl: () => version14, + webgl: () => webgl, + webgl_util: () => webgl_util_exports, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike +}); +var __create = Object.create; +var __defProp2 = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __markAsModule2 = (target) => __defProp2(target, "__esModule", { value: true }); +var __require2 = (x) => { + if (typeof __require !== "undefined") + return __require(x); + throw new Error('Dynamic require of "' + x + '" is not supported'); +}; +var __commonJS = (cb, mod4) => function __require22() { + return mod4 || (0, cb[Object.keys(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports; +}; +var __export2 = (target, all52) => { + __markAsModule2(target); + for (var name in all52) + __defProp2(target, name, { get: all52[name], enumerable: true }); +}; +var __reExport = (target, module, desc) => { + if (module && typeof module === "object" || typeof module === "function") { + for (let key of __getOwnPropNames(module)) + if (!__hasOwnProp.call(target, key) && key !== "default") + __defProp2(target, key, { get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable }); + } + return target; +}; +var __toModule = (module) => { + return __reExport(__markAsModule2(__defProp2(module != null ? __create(__getProtoOf(module)) : {}, "default", module && module.__esModule && "default" in module ? { get: () => module.default, enumerable: true } : { value: module, enumerable: true })), module); +}; +var require_long = __commonJS({ + "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module) { + module.exports = Long2; + var wasm = null; + try { + wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 13, + 2, + 96, + 0, + 1, + 127, + 96, + 4, + 127, + 127, + 127, + 127, + 1, + 127, + 3, + 7, + 6, + 0, + 1, + 1, + 1, + 1, + 1, + 6, + 6, + 1, + 127, + 1, + 65, + 0, + 11, + 7, + 50, + 6, + 3, + 109, + 117, + 108, + 0, + 1, + 5, + 100, + 105, + 118, + 95, + 115, + 0, + 2, + 5, + 100, + 105, + 118, + 95, + 117, + 0, + 3, + 5, + 114, + 101, + 109, + 95, + 115, + 0, + 4, + 5, + 114, + 101, + 109, + 95, + 117, + 0, + 5, + 8, + 103, + 101, + 116, + 95, + 104, + 105, + 103, + 104, + 0, + 0, + 10, + 191, + 1, + 6, + 4, + 0, + 35, + 0, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 126, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 127, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 128, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 129, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 130, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11 + ])), {}).exports; + } catch (e) { + } + function Long2(low, high, unsigned) { + this.low = low | 0; + this.high = high | 0; + this.unsigned = !!unsigned; + } + Long2.prototype.__isLong__; + Object.defineProperty(Long2.prototype, "__isLong__", { value: true }); + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; + } + Long2.isLong = isLong; + var INT_CACHE = {}; + var UINT_CACHE = {}; + function fromInt(value, unsigned) { + var obj, cachedObj, cache; + if (unsigned) { + value >>>= 0; + if (cache = 0 <= value && value < 256) { + cachedObj = UINT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true); + if (cache) + UINT_CACHE[value] = obj; + return obj; + } else { + value |= 0; + if (cache = -128 <= value && value < 128) { + cachedObj = INT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, value < 0 ? -1 : 0, false); + if (cache) + INT_CACHE[value] = obj; + return obj; + } + } + Long2.fromInt = fromInt; + function fromNumber(value, unsigned) { + if (isNaN(value)) + return unsigned ? UZERO : ZERO; + if (unsigned) { + if (value < 0) + return UZERO; + if (value >= TWO_PWR_64_DBL) + return MAX_UNSIGNED_VALUE; + } else { + if (value <= -TWO_PWR_63_DBL) + return MIN_VALUE; + if (value + 1 >= TWO_PWR_63_DBL) + return MAX_VALUE; + } + if (value < 0) + return fromNumber(-value, unsigned).neg(); + return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned); + } + Long2.fromNumber = fromNumber; + function fromBits(lowBits, highBits, unsigned) { + return new Long2(lowBits, highBits, unsigned); + } + Long2.fromBits = fromBits; + var pow_dbl = Math.pow; + function fromString(str, unsigned, radix) { + if (str.length === 0) + throw Error("empty string"); + if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") + return ZERO; + if (typeof unsigned === "number") { + radix = unsigned, unsigned = false; + } else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + var p2; + if ((p2 = str.indexOf("-")) > 0) + throw Error("interior hyphen"); + else if (p2 === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + var radixToPower = fromNumber(pow_dbl(radix, 8)); + var result = ZERO; + for (var i = 0; i < str.length; i += 8) { + var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix); + if (size < 8) { + var power = fromNumber(pow_dbl(radix, size)); + result = result.mul(power).add(fromNumber(value)); + } else { + result = result.mul(radixToPower); + result = result.add(fromNumber(value)); + } + } + result.unsigned = unsigned; + return result; + } + Long2.fromString = fromString; + function fromValue(val, unsigned) { + if (typeof val === "number") + return fromNumber(val, unsigned); + if (typeof val === "string") + return fromString(val, unsigned); + return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned); + } + Long2.fromValue = fromValue; + var TWO_PWR_16_DBL = 1 << 16; + var TWO_PWR_24_DBL = 1 << 24; + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + var ZERO = fromInt(0); + Long2.ZERO = ZERO; + var UZERO = fromInt(0, true); + Long2.UZERO = UZERO; + var ONE = fromInt(1); + Long2.ONE = ONE; + var UONE = fromInt(1, true); + Long2.UONE = UONE; + var NEG_ONE = fromInt(-1); + Long2.NEG_ONE = NEG_ONE; + var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false); + Long2.MAX_VALUE = MAX_VALUE; + var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true); + Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + var MIN_VALUE = fromBits(0, 2147483648 | 0, false); + Long2.MIN_VALUE = MIN_VALUE; + var LongPrototype = Long2.prototype; + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + LongPrototype.toNumber = function toNumber() { + if (this.unsigned) + return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0); + return this.high * TWO_PWR_32_DBL + (this.low >>> 0); + }; + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + if (this.isZero()) + return "0"; + if (this.isNegative()) { + if (this.eq(MIN_VALUE)) { + var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this); + return div3.toString(radix) + rem1.toInt().toString(radix); + } else + return "-" + this.neg().toString(radix); + } + var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this; + var result = ""; + while (true) { + var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix); + rem = remDiv; + if (rem.isZero()) + return digits + result; + else { + while (digits.length < 6) + digits = "0" + digits; + result = "" + digits + result; + } + } + }; + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { + if (this.isNegative()) + return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs(); + var val = this.high != 0 ? this.high : this.low; + for (var bit = 31; bit > 0; bit--) + if ((val & 1 << bit) != 0) + break; + return this.high != 0 ? bit + 33 : bit + 1; + }; + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + LongPrototype.eqz = LongPrototype.isZero; + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + LongPrototype.isEven = function isEven2() { + return (this.low & 1) === 0; + }; + LongPrototype.equals = function equals(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1) + return false; + return this.high === other.high && this.low === other.low; + }; + LongPrototype.eq = LongPrototype.equals; + LongPrototype.notEquals = function notEquals(other) { + return !this.eq(other); + }; + LongPrototype.neq = LongPrototype.notEquals; + LongPrototype.ne = LongPrototype.notEquals; + LongPrototype.lessThan = function lessThan(other) { + return this.comp(other) < 0; + }; + LongPrototype.lt = LongPrototype.lessThan; + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp(other) <= 0; + }; + LongPrototype.lte = LongPrototype.lessThanOrEqual; + LongPrototype.le = LongPrototype.lessThanOrEqual; + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp(other) > 0; + }; + LongPrototype.gt = LongPrototype.greaterThan; + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp(other) >= 0; + }; + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + LongPrototype.compare = function compare(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.eq(other)) + return 0; + var thisNeg = this.isNegative(), otherNeg = other.isNegative(); + if (thisNeg && !otherNeg) + return -1; + if (!thisNeg && otherNeg) + return 1; + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1; + }; + LongPrototype.comp = LongPrototype.compare; + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + LongPrototype.neg = LongPrototype.negate; + LongPrototype.add = function add5(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = addend.high >>> 16; + var b32 = addend.high & 65535; + var b16 = addend.low >>> 16; + var b00 = addend.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 + b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + LongPrototype.sub = LongPrototype.subtract; + LongPrototype.multiply = function multiply4(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + if (wasm) { + var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (multiplier.isZero()) + return ZERO; + if (this.eq(MIN_VALUE)) + return multiplier.isOdd() ? MIN_VALUE : ZERO; + if (multiplier.eq(MIN_VALUE)) + return this.isOdd() ? MIN_VALUE : ZERO; + if (this.isNegative()) { + if (multiplier.isNegative()) + return this.neg().mul(multiplier.neg()); + else + return this.neg().mul(multiplier).neg(); + } else if (multiplier.isNegative()) + return this.mul(multiplier.neg()).neg(); + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 65535; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 65535; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.mul = LongPrototype.multiply; + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error("division by zero"); + if (wasm) { + if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) { + return this; + } + var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (this.isZero()) + return this.unsigned ? UZERO : ZERO; + var approx, rem, res; + if (!this.unsigned) { + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + var halfThis = this.shr(1); + approx = halfThis.div(divisor).shl(1); + if (approx.eq(ZERO)) { + return divisor.isNegative() ? ONE : NEG_ONE; + } else { + rem = this.sub(divisor.mul(approx)); + res = approx.add(rem.div(divisor)); + return res; + } + } + } else if (divisor.eq(MIN_VALUE)) + return this.unsigned ? UZERO : ZERO; + if (this.isNegative()) { + if (divisor.isNegative()) + return this.neg().div(divisor.neg()); + return this.neg().div(divisor).neg(); + } else if (divisor.isNegative()) + return this.div(divisor.neg()).neg(); + res = ZERO; + } else { + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) + return UONE; + res = UZERO; + } + rem = this; + while (rem.gte(divisor)) { + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + var log222 = Math.ceil(Math.log(approx) / Math.LN2), delta = log222 <= 48 ? 1 : pow_dbl(2, log222 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + LongPrototype.div = LongPrototype.divide; + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (wasm) { + var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + return this.sub(this.div(divisor).mul(divisor)); + }; + LongPrototype.mod = LongPrototype.modulo; + LongPrototype.rem = LongPrototype.modulo; + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + LongPrototype.shiftLeft = function shiftLeft(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned); + else + return fromBits(0, this.low << numBits - 32, this.unsigned); + }; + LongPrototype.shl = LongPrototype.shiftLeft; + LongPrototype.shiftRight = function shiftRight(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned); + else + return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned); + }; + LongPrototype.shr = LongPrototype.shiftRight; + LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + numBits &= 63; + if (numBits === 0) + return this; + else { + var high = this.high; + if (numBits < 32) { + var low = this.low; + return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned); + } else if (numBits === 32) + return fromBits(high, 0, this.unsigned); + else + return fromBits(high >>> numBits - 32, 0, this.unsigned); + } + }; + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 255, + lo >>> 8 & 255, + lo >>> 16 & 255, + lo >>> 24, + hi & 255, + hi >>> 8 & 255, + hi >>> 16 & 255, + hi >>> 24 + ]; + }; + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 255, + hi >>> 8 & 255, + hi & 255, + lo >>> 24, + lo >>> 16 & 255, + lo >>> 8 & 255, + lo & 255 + ]; + }; + Long2.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned); + }; + Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long2(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned); + }; + Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long2(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned); + }; + } +}); +var require_browser = __commonJS({ + "(disabled):node_modules/.pnpm/node-fetch@2.6.1/node_modules/node-fetch/browser.js"() { + } +}); +var require_alea = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(exports, module) { + (function(global2, module2, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = data.toString(); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor128 = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorwow = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorshift7 = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor4096 = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = t == 0 ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_tychei = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_crypto = __commonJS({ + "(disabled):crypto"() { + } +}); +var require_seedrandom = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(exports, module) { + (function(pool3, math) { + var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options2, callback) { + var key = []; + options2 = options2 == true ? { entropy: true } : options2 || {}; + var shortseed = mixkey(flatten4(options2.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options2.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })(prng, shortseed, "global" in options2 ? options2.global : this == math, options2.state); + } + math["seed" + rngname] = seedrandom5; + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count22) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count22--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser = global2.navigator, plugins = browser && browser.plugins; + return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module == "object" && module.exports) { + module.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } + })([], Math); + } +}); +var require_seedrandom2 = __commonJS({ + "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(exports, module) { + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module.exports = sr; + } +}); +var require_alea2 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module) { + (function(global2, module2, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = String(data); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor1282 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorwow2 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorshift72 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor40962 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = t == 0 ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_tychei2 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_seedrandom3 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module) { + (function(global2, pool3, math) { + var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options2, callback) { + var key = []; + options2 = options2 == true ? { entropy: true } : options2 || {}; + var shortseed = mixkey(flatten4(options2.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options2.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })(prng, shortseed, "global" in options2 ? options2.global : this == math, options2.state); + } + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count22) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count22--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser = global2.navigator, plugins = browser && browser.plugins; + return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module == "object" && module.exports) { + module.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } else { + math["seed" + rngname] = seedrandom5; + } + })(typeof self !== "undefined" ? self : exports, [], Math); + } +}); +var require_seedrandom4 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module) { + var alea5 = require_alea2(); + var xor128 = require_xor1282(); + var xorwow = require_xorwow2(); + var xorshift7 = require_xorshift72(); + var xor4096 = require_xor40962(); + var tychei = require_tychei2(); + var sr = require_seedrandom3(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module.exports = sr; + } +}); +var require_string_decoder = __commonJS({ + "(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"() { + } +}); +var require_path = __commonJS({ + "(disabled):path"() { + } +}); +var require_worker_threads = __commonJS({ + "(disabled):worker_threads"() { + } +}); +var require_perf_hooks = __commonJS({ + "(disabled):perf_hooks"() { + } +}); +var require_tfjs_backend_wasm_threaded_simd = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.8.0_@tensorflow+tfjs-core@3.8.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) { + var WasmBackendModuleThreadedSimd = function() { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModuleThreadedSimd2) { + WasmBackendModuleThreadedSimd2 = WasmBackendModuleThreadedSimd2 || {}; + function GROWABLE_HEAP_I8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP8; + } + function GROWABLE_HEAP_U8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU8; + } + function GROWABLE_HEAP_I32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP32; + } + function GROWABLE_HEAP_U32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU32; + } + function GROWABLE_HEAP_F64() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF64; + } + var Module = typeof WasmBackendModuleThreadedSimd2 !== "undefined" ? WasmBackendModuleThreadedSimd2 : {}; + 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) { + buffer2 = 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 = __require2("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(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 readBinary2(f) { + var data; + if (typeof readbuffer === "function") { + return new Uint8Array(readbuffer(f)); + } + data = read(f, "binary"); + assert3(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 = __require2("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(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 assert3(condition, text) { + if (!condition) { + abort("Assertion failed: " + text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported"); + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + ret = convertReturnValue(ret); + if (stack2 !== 0) + stackRestore(stack2); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var str = ""; + while (!(idx >= 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 < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 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 < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 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(array2, buffer3) { + GROWABLE_HEAP_I8().set(array2, buffer3); + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = 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"]; + buffer2 = 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) { + buffer2 = wasmMemory.buffer; + } + INITIAL_MEMORY = buffer2.byteLength; + updateGlobalBufferAndViews(buffer2); + 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(); + } }); + 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; + if (ENVIRONMENT_IS_PTHREAD) + return; + 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) { + assert3(!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 (err2) { + abort(err2); + } + } + 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 info2 = { "a": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmTable = Module["asm"]["F"]; + wasmModule = module2; + 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, info2); + }).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, info2); + 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 exports2 = Module["instantiateWasm"](info2, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var ASM_CONSTS = { 9832: function() { + throw "Canceled!"; + }, 9850: function($0, $1) { + setTimeout(function() { + __emscripten_do_dispatch_to_thread($0, $1); + }, 0); + } }; + function initPthreadsJS() { + PThread.initRuntime(); + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + wasmTable.get(func2)(); + } else { + wasmTable.get(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? null : callback.arg); + } + } + } + function _emscripten_futex_wake(addr, count22) { + if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true || count22 < 0) + return -28; + if (count22 == 0) + return 0; + if (count22 >= 2147483647) + count22 = 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) { + --count22; + mainThreadWoken = 1; + if (count22 <= 0) + return 1; + } + } + var ret = Atomics.notify(GROWABLE_HEAP_I32(), addr >> 2, count22); + 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 = void 0; + } + 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!"; + var pthread = PThread.pthreads[pthread_ptr]; + if (pthread) { + GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0; + var worker = pthread.worker; + PThread.returnWorkerToPool(worker); + } + } + var PThread = { unusedWorkers: [], runningWorkers: [], initMainThreadBlock: function() { + var pthreadPoolSize = Math.min(4, Math.max(1, (navigator.hardwareConcurrency || 1) / 2)); + for (var i = 0; i < pthreadPoolSize; ++i) { + PThread.allocateUnusedWorker(); + } + }, initRuntime: function() { + var tb = _malloc(228); + for (var i = 0; i < 228 / 4; ++i) + GROWABLE_HEAP_U32()[tb / 4 + i] = 0; + GROWABLE_HEAP_I32()[tb + 12 >> 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(); + }, runExitHandlersAndDeinitThread: function(tb, exitCode) { + Atomics.store(GROWABLE_HEAP_U32(), tb + 56 >> 2, 1); + Atomics.store(GROWABLE_HEAP_U32(), tb + 60 >> 2, 0); + PThread.runExitHandlers(); + Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, exitCode); + Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1); + _emscripten_futex_wake(tb + 0, 2147483647); + __emscripten_thread_init(0, 0, 0); + }, threadExit: function(exitCode) { + var tb = _pthread_self(); + if (tb) { + PThread.runExitHandlersAndDeinitThread(tb, exitCode); + if (ENVIRONMENT_IS_PTHREAD) { + postMessage({ "cmd": "exit" }); + } + } + }, threadCancel: function() { + PThread.runExitHandlersAndDeinitThread(_pthread_self(), -1); + 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 < PThread.unusedWorkers.length; ++i) { + var worker = PThread.unusedWorkers[i]; + worker.terminate(); + } + PThread.unusedWorkers = []; + for (var i = 0; i < PThread.runningWorkers.length; ++i) { + var worker = PThread.runningWorkers[i]; + var pthread = worker.pthread; + PThread.freeThreadData(pthread); + worker.terminate(); + } + PThread.runningWorkers = []; + }, freeThreadData: function(pthread) { + if (!pthread) + return; + if (pthread.threadInfoStruct) { + var tlsMemory = GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 100 >> 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 = void 0; + }); + }, runWithoutMainThreadQueuedCalls: function(func2) { + GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0; + try { + func2(); + } 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 = void 0; + 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 (e2) { + if (e2 instanceof ExitStatus) + return; + throw e2; + } + } 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 = void 0; + }; + 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 }); + }); + 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() < t) { + } + } }; + function establishStackSpace(stackTop, stackMax) { + _emscripten_stack_set_limits(stackTop, stackMax); + stackRestore(stackTop); + } + Module["establishStackSpace"] = establishStackSpace; + function getNoExitRuntime() { + return noExitRuntime; + } + Module["getNoExitRuntime"] = getNoExitRuntime; + function invokeEntryPoint(ptr, arg) { + return wasmTable.get(ptr)(arg); + } + Module["invokeEntryPoint"] = invokeEntryPoint; + function ___assert_fail(condition, filename, line, func2) { + abort("Assertion failed: " + UTF8ToString(condition) + ", at: " + [filename ? UTF8ToString(filename) : "unknown filename", line, func2 ? UTF8ToString(func2) : "unknown function"]); + } + function ___call_main(argc, argv) { + var returnCode = _main(argc, argv); + } + var _emscripten_get_now; + if (ENVIRONMENT_IS_NODE) { + _emscripten_get_now = function() { + var t = process["hrtime"](); + return t[0] * 1e3 + t[1] / 1e6; + }; + } else if (ENVIRONMENT_IS_PTHREAD) { + _emscripten_get_now = function() { + return performance.now() - Module["__performance_now_clock_drift"]; + }; + } else if (typeof dateNow !== "undefined") { + _emscripten_get_now = dateNow; + } else + _emscripten_get_now = function() { + return performance.now(); + }; + function setErrNo(value) { + GROWABLE_HEAP_I32()[___errno_location() >> 2] = value; + return value; + } + function _atexit(func2, arg) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(1, 1, func2, 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 & true) + 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 __require2("os").cpus().length; + return navigator["hardwareConcurrency"]; + } + function _emscripten_proxy_to_main_thread_js(index, sync) { + var numCallArgs = arguments.length - 2; + var stack2 = stackSave(); + var serializedNumCallArgs = numCallArgs; + var args = stackAlloc(serializedNumCallArgs * 8); + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + var arg = arguments[2 + i]; + GROWABLE_HEAP_F64()[b + i] = arg; + } + var ret = _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync); + stackRestore(stack2); + return ret; + } + var _emscripten_receive_on_main_thread_js_callArgs = []; + var readAsmConstArgsArray = []; + function readAsmConstArgs(sigPtr, buf) { + readAsmConstArgsArray.length = 0; + var ch; + buf >>= 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 < numCallArgs; i++) { + _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i]; + } + var isEmAsmConst = index < 0; + var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1]; + return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs); + } + function _emscripten_get_heap_size() { + return GROWABLE_HEAP_U8().length; + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 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 + 0.2 / 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 i2 in arrA) { + if (arrA[i2] != arrB[i2]) + 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, precedence, argsList }); + JSEvents.deferredCalls.sort(function(x, y) { + return x.precedence < y.precedence; + }); + }, removeDeferredCalls: function(targetFunction) { + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + if (JSEvents.deferredCalls[i].targetFunction == targetFunction) { + JSEvents.deferredCalls.splice(i, 1); + --i; + } + } + }, canPerformEventHandlerRequests: function() { + return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls; + }, runDeferredCalls: function() { + if (!JSEvents.canPerformEventHandlerRequests()) { + return; + } + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + var call = JSEvents.deferredCalls[i]; + JSEvents.deferredCalls.splice(i, 1); + --i; + call.targetFunction.apply(null, call.argsList); + } + }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) { + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) { + JSEvents._removeHandler(i--); + } + } + }, _removeHandler: function(i) { + var h = JSEvents.eventHandlers[i]; + h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture); + JSEvents.eventHandlers.splice(i, 1); + }, registerOrRemoveHandler: function(eventHandler) { + var jsEventHandler = function jsEventHandler2(event) { + ++JSEvents.inEventHandler; + JSEvents.currentEventHandler = eventHandler; + JSEvents.runDeferredCalls(); + eventHandler.handlerFunc(event); + JSEvents.runDeferredCalls(); + --JSEvents.inEventHandler; + }; + if (eventHandler.callbackfunc) { + eventHandler.eventListenerFunc = jsEventHandler; + eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture); + JSEvents.eventHandlers.push(eventHandler); + JSEvents.registerRemoveEventListeners(); + } else { + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) { + JSEvents._removeHandler(i--); + } + } + } + }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) { + var stackTop = stackSave(); + var varargs = stackAlloc(12); + GROWABLE_HEAP_I32()[varargs >> 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) : void 0); + return domElement; + } + function findCanvasEventTarget(target) { + return findEventTarget(target); + } + function _emscripten_set_canvas_element_size_calling_thread(target, width, height) { + var canvas2 = findCanvasEventTarget(target); + if (!canvas2) + return -4; + if (canvas2.canvasSharedPtr) { + GROWABLE_HEAP_I32()[canvas2.canvasSharedPtr >> 2] = width; + GROWABLE_HEAP_I32()[canvas2.canvasSharedPtr + 4 >> 2] = height; + } + if (canvas2.offscreenCanvas || !canvas2.controlTransferredOffscreen) { + if (canvas2.offscreenCanvas) + canvas2 = canvas2.offscreenCanvas; + var autoResizeViewport = false; + if (canvas2.GLctxObject && canvas2.GLctxObject.GLctx) { + var prevViewport = canvas2.GLctxObject.GLctx.getParameter(2978); + autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas2.width && prevViewport[3] === canvas2.height; + } + canvas2.width = width; + canvas2.height = height; + if (autoResizeViewport) { + canvas2.GLctxObject.GLctx.viewport(0, 0, width, height); + } + } else if (canvas2.canvasSharedPtr) { + var targetThread = GROWABLE_HEAP_I32()[canvas2.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 canvas2 = findCanvasEventTarget(target); + if (canvas2) { + 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, count22, primcount) { + ext["drawArraysInstancedANGLE"](mode, first, count22, primcount); + }; + ctx["drawElementsInstanced"] = function(mode, count22, type, indices, primcount) { + ext["drawElementsInstancedANGLE"](mode, count22, 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 < ret; i++) { + table[i] = null; + } + return ret; + }, getSource: function(shader, count22, string3, length) { + var source = ""; + for (var i = 0; i < count22; ++i) { + var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1; + source += UTF8ToString(GROWABLE_HEAP_I32()[string3 + i * 4 >> 2], len < 0 ? void 0 : len); + } + return source; + }, createContext: function(canvas2, webGLContextAttributes) { + var ctx = canvas2.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, 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 = void 0; + _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 GLctx2 = context.GLctx; + __webgl_enable_ANGLE_instanced_arrays(GLctx2); + __webgl_enable_OES_vertex_array_object(GLctx2); + __webgl_enable_WEBGL_draw_buffers(GLctx2); + GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query"); + __webgl_enable_WEBGL_multi_draw(GLctx2); + var exts = GLctx2.getSupportedExtensions() || []; + exts.forEach(function(ext) { + if (ext.indexOf("lose_context") < 0 && ext.indexOf("debug") < 0) { + GLctx2.getExtension(ext); + } + }); + }, populateUniformTable: function(program) { + var p2 = GL.programs[program]; + var ptable = GL.programInfos[program] = { uniforms: {}, maxUniformLength: 0, maxAttributeLength: -1, maxUniformBlockNameLength: -1 }; + var utable = ptable.uniforms; + var numUniforms = GLctx.getProgramParameter(p2, 35718); + for (var i = 0; i < numUniforms; ++i) { + var u = GLctx.getActiveUniform(p2, i); + var name = u.name; + ptable.maxUniformLength = Math.max(ptable.maxUniformLength, name.length + 1); + if (name.slice(-1) == "]") { + name = name.slice(0, name.lastIndexOf("[")); + } + var loc = GLctx.getUniformLocation(p2, name); + if (loc) { + var id = GL.getNewId(GL.uniforms); + utable[name] = [u.size, id]; + GL.uniforms[id] = loc; + for (var j = 1; j < u.size; ++j) { + var n = name + "[" + j + "]"; + loc = GLctx.getUniformLocation(p2, n); + id = GL.getNewId(GL.uniforms); + GL.uniforms[id] = loc; + } + } + } + } }; + var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"]; + function _emscripten_webgl_do_create_context(target, attributes) { + var a = attributes >> 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 canvas2 = findCanvasEventTarget(target); + if (!canvas2) { + return 0; + } + if (contextAttributes.explicitSwapControl) { + return 0; + } + var contextHandle = GL.createContext(canvas2, contextAttributes); + return contextHandle; + } + function _emscripten_webgl_create_context(a0, a12) { + return _emscripten_webgl_do_create_context(a0, a12); + } + var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) { + var buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, 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 < iovcnt; i++) { + var ptr = GROWABLE_HEAP_I32()[iov + i * 8 >> 2]; + var len = GROWABLE_HEAP_I32()[iov + (i * 8 + 4) >> 2]; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]); + } + num += len; + } + GROWABLE_HEAP_I32()[pnum >> 2] = num; + return 0; + } + function _pthread_cleanup_pop(execute2) { + var routine = PThread.threadExitHandlers.pop(); + if (execute2) + 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 !== void 0) + 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, 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; + assert3(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, stackSize, allocatedOwnStack, detached, startRoutine: start_routine, pthread_ptr: threadInfoStruct, arg, 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 _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["J"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["K"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["L"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["M"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["N"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["O"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["P"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Q"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["R"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["S"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["T"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["U"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["V"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["W"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["X"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Y"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Z"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["_"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["$"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["aa"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["ba"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["ca"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["da"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["ea"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["fa"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["ga"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["ha"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["ia"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["ja"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["ka"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["la"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["ma"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["na"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["oa"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["pa"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["qa"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["ra"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["sa"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["ta"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["ua"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["va"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["wa"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["xa"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ya"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["za"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["Aa"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["Ba"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Ca"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Da"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Ea"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["Fa"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Ga"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Ha"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["Ia"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Ja"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Ka"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["La"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Ma"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Na"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Oa"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Pa"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Qa"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Ra"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sa"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Ta"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["Ua"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Va"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["Wa"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Xa"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Ya"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Za"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["_a"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["$a"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["ab"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["bb"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["cb"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["db"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["eb"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["fb"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["gb"]).apply(null, arguments); + }; + var _emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = function() { + return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["hb"]).apply(null, arguments); + }; + var _pthread_self = Module["_pthread_self"] = function() { + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["ib"]).apply(null, arguments); + }; + var ___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = function() { + return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["jb"]).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"]["kb"]).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"]["lb"]).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"]["mb"]).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"]["nb"]).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"]["ob"]).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"]["pb"]).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"]["qb"]).apply(null, arguments); + }; + var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() { + return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["rb"]).apply(null, arguments); + }; + var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["sb"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["tb"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["ub"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["vb"]).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"]["wb"]).apply(null, arguments); + }; + var _memalign = Module["_memalign"] = function() { + return (_memalign = Module["_memalign"] = Module["asm"]["xb"]).apply(null, arguments); + }; + var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 9824; + var __emscripten_main_thread_futex = Module["__emscripten_main_thread_futex"] = 11448; + 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); + initRuntime(); + 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(); + return WasmBackendModuleThreadedSimd2.ready; + }; + }(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModuleThreadedSimd; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModuleThreadedSimd; + }); + else if (typeof exports === "object") + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd; + } +}); +var require_tfjs_backend_wasm = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.8.0_@tensorflow+tfjs-core@3.8.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) { + var WasmBackendModule = function() { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModule2) { + WasmBackendModule2 = WasmBackendModule2 || {}; + var Module = typeof WasmBackendModule2 !== "undefined" ? WasmBackendModule2 : {}; + 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 = __require2("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(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 readBinary2(f) { + var data; + if (typeof readbuffer === "function") { + return new Uint8Array(readbuffer(f)); + } + data = read(f, "binary"); + assert3(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 assert3(condition, text) { + if (!condition) { + abort("Assertion failed: " + text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported"); + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + ret = convertReturnValue(ret); + if (stack2 !== 0) + stackRestore(stack2); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heap[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) { + return UTF8Decoder.decode(heap.subarray(idx, endPtr)); + } else { + var str = ""; + while (idx < endPtr) { + var u0 = heap[idx++]; + 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(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 < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 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(array2, buffer3) { + HEAP8.set(array2, buffer3); + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = 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 (err2) { + abort(err2); + } + } + 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 info2 = { "a": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmMemory = Module["asm"]["i"]; + updateGlobalBufferAndViews(wasmMemory.buffer); + wasmTable = Module["asm"]["o"]; + removeRunDependency("wasm-instantiate"); + } + addRunDependency("wasm-instantiate"); + function receiveInstantiatedSource(output) { + receiveInstance(output["instance"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info2); + }).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, info2); + 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 exports2 = Module["instantiateWasm"](info2, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + wasmTable.get(func2)(); + } else { + wasmTable.get(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? 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 - buffer2.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 + 0.2 / 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 buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, 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 < iovcnt; i++) { + var ptr = HEAP32[iov + i * 8 >> 2]; + var len = HEAP32[iov + (i * 8 + 4) >> 2]; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, HEAPU8[ptr + j]); + } + num += len; + } + HEAP32[pnum >> 2] = num; + return 0; + } + function _pthread_create() { + return 6; + } + function setErrNo(value) { + HEAP32[___errno_location() >> 2] = value; + return value; + } + function _sysconf(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; + } + var asmLibraryArg = { "a": _abort, "d": _emscripten_memcpy_big, "e": _emscripten_resize_heap, "f": _fd_close, "c": _fd_seek, "b": _fd_write, "g": _pthread_create, "h": _sysconf }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["j"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["k"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["l"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["m"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["n"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["p"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["q"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["r"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["s"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["t"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["u"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["v"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["w"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["x"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["y"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["z"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["A"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["B"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["C"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["D"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["E"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["F"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["G"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["H"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["I"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["J"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["K"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["L"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["M"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["N"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["O"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["P"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["Q"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["R"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["S"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["T"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["U"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["V"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["W"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["X"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Y"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["Z"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["_"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["$"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["aa"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["ba"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["ca"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["da"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["ea"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["fa"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ga"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ha"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ia"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["ja"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["ka"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["la"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["ma"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["na"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["oa"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["pa"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["qa"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ra"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["sa"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["ta"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["ua"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["va"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["wa"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["xa"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["ya"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["za"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Aa"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Ba"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Ca"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["Da"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Ea"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["Fa"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Ga"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Ha"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Ia"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Ja"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Ka"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["La"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Ma"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Na"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Oa"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["Pa"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["Qa"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["Ra"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["Sa"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["Ta"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["Ua"]).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(); + return WasmBackendModule2.ready; + }; + }(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModule; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModule; + }); + else if (typeof exports === "object") + exports["WasmBackendModule"] = WasmBackendModule; + } +}); +var version = "3.8.0"; +var version2 = "3.8.0"; +var version3 = "3.8.0"; +var version4 = "3.8.0"; +var version5 = "3.8.0"; +var version6 = "3.8.0"; +var version7 = "3.8.0"; +var version8 = "3.8.0"; +var EPSILON_FLOAT32 = 1e-7; +var EPSILON_FLOAT16 = 1e-4; +var DataStorage = class { + constructor(backend22, dataMover) { + this.backend = backend22; + this.dataMover = dataMover; + this.data = new WeakMap(); + this.dataIdsCount = 0; + } + get(dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); + } + set(dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + } + has(dataId) { + return this.data.has(dataId); + } + delete(dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); + } + numDataIds() { + return this.dataIdsCount; + } +}; +var KernelBackend = class { + refCount(dataId) { + return notYetImplemented("refCount"); + } + incRef(dataId) { + return notYetImplemented("incRef"); + } + timerAvailable() { + return true; + } + time(f) { + return notYetImplemented("time"); + } + read(dataId) { + return notYetImplemented("read"); + } + readSync(dataId) { + return notYetImplemented("readSync"); + } + numDataIds() { + return notYetImplemented("numDataIds"); + } + disposeData(dataId, force) { + return notYetImplemented("disposeData"); + } + write(values, shape, dtype) { + return notYetImplemented("write"); + } + move(dataId, values, shape, dtype, refCount) { + return notYetImplemented("move"); + } + memory() { + return notYetImplemented("memory"); + } + floatPrecision() { + return notYetImplemented("floatPrecision"); + } + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + } + dispose() { + return notYetImplemented("dispose"); + } +}; +function notYetImplemented(kernelName) { + throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +} +function shuffle(array2) { + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + } +} +function shuffleCombo(array2, array22) { + if (array2.length !== array22.length) { + throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`); + } + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + swap(array22, counter, index); + } +} +function clamp(min6, x, max6) { + return Math.max(min6, Math.min(x, max6)); +} +function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; +} +function swap(object2, left, right) { + const temp = object2[left]; + object2[left] = object2[right]; + object2[right] = temp; +} +function sum(arr) { + let sum6 = 0; + for (let i = 0; i < arr.length; i++) { + sum6 += arr[i]; + } + return sum6; +} +function randUniform(a, b) { + const r = Math.random(); + return b * r + (1 - r) * a; +} +function distSquared(a, b) { + let result = 0; + for (let i = 0; i < a.length; i++) { + const diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; +} +function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === "string" ? msg : msg()); + } +} +function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") { + assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); +} +function assertNonNull(a) { + assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); +} +function flatten(arr, result = [], skipTypedArray = false) { + if (result == null) { + result = []; + } + if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) { + for (let i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } else { + result.push(arr); + } + return result; +} +function sizeFromShape(shape) { + if (shape.length === 0) { + return 1; + } + let size = shape[0]; + for (let i = 1; i < shape.length; i++) { + size *= shape[i]; + } + return size; +} +function isScalarShape(shape) { + return shape.length === 0; +} +function arraysEqual(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function isInt(a) { + return a % 1 === 0; +} +function tanh(x) { + if (Math.tanh != null) { + return Math.tanh(x); + } + if (x === Infinity) { + return 1; + } else if (x === -Infinity) { + return -1; + } else { + const e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); + } +} +function sizeToSquarishShape(size) { + const width = Math.ceil(Math.sqrt(size)); + return [width, Math.ceil(size / width)]; +} +function createShuffledIndices(n) { + const shuffledIndices = new Uint32Array(n); + for (let i = 0; i < n; ++i) { + shuffledIndices[i] = i; + } + shuffle(shuffledIndices); + return shuffledIndices; +} +function rightPad(a, size) { + if (size <= a.length) { + return a; + } + return a + " ".repeat(size - a.length); +} +function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) { + return new Promise((resolve, reject) => { + let tryCount = 0; + const tryFn = () => { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + const nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + setTimeout(tryFn, nextBackoff); + }; + tryFn(); + }); +} +function inferFromImplicitShape(shape, size) { + let shapeProd = 1; + let implicitIdx = -1; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] >= 0) { + shapeProd *= shape[i]; + } else if (shape[i] === -1) { + if (implicitIdx !== -1) { + throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`); + } + implicitIdx = i; + } else if (shape[i] < 0) { + throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`); + } + } + if (implicitIdx === -1) { + if (size > 0 && size !== shapeProd) { + throw Error(`Size(${size}) must match the product of shape ${shape}`); + } + return shape; + } + if (shapeProd === 0) { + throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`); + } + if (size % shapeProd !== 0) { + throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`); + } + const newShape = shape.slice(); + newShape[implicitIdx] = size / shapeProd; + return newShape; +} +function parseAxisParam(axis, shape) { + const rank = shape.length; + axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); + assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`); + assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`); + return axis.map((a) => a < 0 ? rank + a : a); +} +function squeezeShape(shape, axis) { + const newShape = []; + const keptDims = []; + const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort(); + let j = 0; + for (let i = 0; i < shape.length; ++i) { + if (axes != null) { + if (axes[j] === i && shape[i] !== 1) { + throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); + } + if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + if (axes[j] <= i) { + j++; + } + } + if (shape[i] !== 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + } + return { newShape, keptDims }; +} +function getTypedArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function getArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else if (dtype === "string") { + values = new Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function checkConversionForErrors(vals, dtype) { + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); + } + } +} +function isValidDtype(dtype) { + return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string"; +} +function hasEncodingLoss(oldType, newType) { + if (newType === "complex64") { + return false; + } + if (newType === "float32" && oldType !== "complex64") { + return false; + } + if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") { + return false; + } + if (newType === "bool" && oldType === "bool") { + return false; + } + return true; +} +function isTypedArray(a) { + return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array; +} +function bytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else if (dtype === "bool") { + return 1; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + let bytes = 0; + arr.forEach((x) => bytes += x.length); + return bytes; +} +function isString(value) { + return typeof value === "string" || value instanceof String; +} +function isBoolean(value) { + return typeof value === "boolean"; +} +function isNumber(value) { + return typeof value === "number"; +} +function inferDtype(values) { + if (Array.isArray(values)) { + return inferDtype(values[0]); + } + if (values instanceof Float32Array) { + return "float32"; + } else if (values instanceof Int32Array || values instanceof Uint8Array) { + return "int32"; + } else if (isNumber(values)) { + return "float32"; + } else if (isString(values)) { + return "string"; + } else if (isBoolean(values)) { + return "bool"; + } + return "float32"; +} +function isFunction(f) { + return !!(f && f.constructor && f.call && f.apply); +} +function nearestDivisor(size, start) { + for (let i = start; i < size; ++i) { + if (size % i === 0) { + return i; + } + } + return size; +} +function computeStrides(shape) { + const rank = shape.length; + if (rank < 2) { + return []; + } + const strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; +} +function createNestedArray(offset, shape, a, isComplex = false) { + const ret = new Array(); + if (shape.length === 1) { + const d = shape[0] * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } else { + const d = shape[0]; + const rest = shape.slice(1); + const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; +} +function toNestedArray(shape, a, isComplex = false) { + if (shape.length === 0) { + return a[0]; + } + const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + if (size === 0) { + return []; + } + if (size !== a.length) { + throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`); + } + return createNestedArray(0, shape, a, isComplex); +} +function makeOnesTypedArray(size, dtype) { + const array2 = makeZerosTypedArray(size, dtype); + for (let i = 0; i < array2.length; i++) { + array2[i] = 1; + } + return array2; +} +function makeZerosTypedArray(size, dtype) { + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(size); + } else if (dtype === "int32") { + return new Int32Array(size); + } else if (dtype === "bool") { + return new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function makeZerosNestedTypedArray(shape, dtype) { + const size = shape.reduce((prev, curr) => prev * curr, 1); + if (dtype == null || dtype === "float32") { + return toNestedArray(shape, new Float32Array(size)); + } else if (dtype === "int32") { + return toNestedArray(shape, new Int32Array(size)); + } else if (dtype === "bool") { + return toNestedArray(shape, new Uint8Array(size)); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function assertNonNegativeIntegerDimensions(shape) { + shape.forEach((dimSize) => { + assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`); + }); +} +function locToIndex(locs, rank, strides) { + if (rank === 0) { + return 0; + } else if (rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += strides[i] * locs[i]; + } + return index; +} +function indexToLoc(index, rank, strides) { + if (rank === 0) { + return []; + } else if (rank === 1) { + return [index]; + } + const locs = new Array(rank); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / strides[i]); + index -= locs[i] * strides[i]; + } + locs[locs.length - 1] = index; + return locs; +} +function isPromise(object2) { + return object2 && object2.then && typeof object2.then === "function"; +} +var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; +var Environment = class { + constructor(global2) { + this.global = global2; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + setPlatform(platformName, platform) { + if (this.platform != null) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`); + } + this.platformName = platformName; + this.platform = platform; + } + registerFlag(flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn, setHook }; + if (this.urlFlags[flagName] != null) { + const flagValue = this.urlFlags[flagName]; + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + this.set(flagName, flagValue); + } + } + async getAsync(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + this.flags[flagName] = await this.evaluateFlag(flagName); + return this.flags[flagName]; + } + get(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + const flagValue = this.evaluateFlag(flagName); + if (isPromise(flagValue)) { + throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`); + } + this.flags[flagName] = flagValue; + return this.flags[flagName]; + } + getNumber(flagName) { + return this.get(flagName); + } + getBool(flagName) { + return this.get(flagName); + } + getFlags() { + return this.flags; + } + get features() { + return this.flags; + } + set(flagName, value) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); + } + this.flags[flagName] = value; + if (this.flagRegistry[flagName].setHook != null) { + this.flagRegistry[flagName].setHook(value); + } + } + evaluateFlag(flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); + } + return this.flagRegistry[flagName].evaluationFn(); + } + setFlags(flags) { + this.flags = Object.assign({}, flags); + } + reset() { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + } + populateURLFlags() { + if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") { + return; + } + const urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(","); + keyValues.forEach((keyValue) => { + const [key, value] = keyValue.split(":"); + this.urlFlags[key] = parseValue(key, value); + }); + } + } +}; +function getQueryParams(queryString) { + const params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { + decodeParam(params, t[0], t[1]); + return t.join("="); + }); + return params; +} +function decodeParam(params, name, value) { + params[decodeURIComponent(name)] = decodeURIComponent(value || ""); +} +function parseValue(flagName, value) { + value = value.toLowerCase(); + if (value === "true" || value === "false") { + return value === "true"; + } else if (`${+value}` === value) { + return +value; + } + throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`); +} +function env() { + return ENV; +} +var ENV = null; +function setEnvironmentGlobal(environment) { + ENV = environment; +} +var globalNameSpace; +function getGlobalNamespace() { + if (globalNameSpace == null) { + let ns; + if (typeof window !== "undefined") { + ns = window; + } else if (typeof global !== "undefined") { + ns = global; + } else if (typeof process !== "undefined") { + ns = process; + } else if (typeof self !== "undefined") { + ns = self; + } else { + throw new Error("Could not find a global object"); + } + globalNameSpace = ns; + } + return globalNameSpace; +} +function getGlobalMap() { + const ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = new Map(); + } + return ns._tfGlobals; +} +function getGlobal(key, init2) { + const globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } else { + const singleton = init2(); + globalMap.set(key, singleton); + return globalMap.get(key); + } +} +var Abs = "Abs"; +var Acos = "Acos"; +var Acosh = "Acosh"; +var Add = "Add"; +var AddN = "AddN"; +var All = "All"; +var Any = "Any"; +var ArgMax = "ArgMax"; +var ArgMin = "ArgMin"; +var Asin = "Asin"; +var Asinh = "Asinh"; +var Atan = "Atan"; +var Atanh = "Atanh"; +var Atan2 = "Atan2"; +var AvgPool = "AvgPool"; +var AvgPoolGrad = "AvgPoolGrad"; +var AvgPool3D = "AvgPool3D"; +var AvgPool3DGrad = "AvgPool3DGrad"; +var BatchMatMul = "BatchMatMul"; +var BatchToSpaceND = "BatchToSpaceND"; +var Bincount = "Bincount"; +var BroadcastTo = "BroadcastTo"; +var Cast = "Cast"; +var Ceil = "Ceil"; +var ClipByValue = "ClipByValue"; +var Complex = "Complex"; +var ComplexAbs = "ComplexAbs"; +var Concat = "Concat"; +var Conv2D = "Conv2D"; +var Conv2DBackpropFilter = "Conv2DBackpropFilter"; +var Conv2DBackpropInput = "Conv2DBackpropInput"; +var Conv3D = "Conv3D"; +var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2"; +var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2"; +var Cos = "Cos"; +var Cosh = "Cosh"; +var Cumsum = "Cumsum"; +var CropAndResize = "CropAndResize"; +var DenseBincount = "DenseBincount"; +var DepthToSpace = "DepthToSpace"; +var DepthwiseConv2dNative = "DepthwiseConv2dNative"; +var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter"; +var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput"; +var Diag = "Diag"; +var Dilation2D = "Dilation2D"; +var Dilation2DBackpropInput = "Dilation2DBackpropInput"; +var Dilation2DBackpropFilter = "Dilation2DBackpropFilter"; +var RealDiv = "RealDiv"; +var Einsum = "Einsum"; +var Elu = "Elu"; +var EluGrad = "EluGrad"; +var Erf = "Erf"; +var Equal = "Equal"; +var Exp = "Exp"; +var ExpandDims = "ExpandDims"; +var Expm1 = "Expm1"; +var FFT = "FFT"; +var Fill = "Fill"; +var FlipLeftRight = "FlipLeftRight"; +var Floor = "Floor"; +var FloorDiv = "FloorDiv"; +var FusedBatchNorm = "FusedBatchNorm"; +var GatherV2 = "GatherV2"; +var GatherNd = "GatherNd"; +var Greater = "Greater"; +var GreaterEqual = "GreaterEqual"; +var Identity = "Identity"; +var IFFT = "IFFT"; +var Imag = "Imag"; +var IsFinite = "IsFinite"; +var IsInf = "IsInf"; +var IsNan = "IsNan"; +var LeakyRelu = "LeakyRelu"; +var Less = "Less"; +var LessEqual = "LessEqual"; +var LinSpace = "LinSpace"; +var Log = "Log"; +var Log1p = "Log1p"; +var LogicalAnd = "LogicalAnd"; +var LogicalNot = "LogicalNot"; +var LogicalOr = "LogicalOr"; +var LogSoftmax = "LogSoftmax"; +var LRN = "LRN"; +var LRNGrad = "LRNGrad"; +var Max = "Max"; +var Maximum = "Maximum"; +var MaxPool = "MaxPool"; +var MaxPoolGrad = "MaxPoolGrad"; +var MaxPool3D = "MaxPool3D"; +var MaxPool3DGrad = "MaxPool3DGrad"; +var MaxPoolWithArgmax = "MaxPoolWithArgmax"; +var Mean = "Mean"; +var Min = "Min"; +var Minimum = "Minimum"; +var MirrorPad = "MirrorPad"; +var Mod = "Mod"; +var Multinomial = "Multinomial"; +var Multiply = "Multiply"; +var Neg = "Neg"; +var NotEqual = "NotEqual"; +var NonMaxSuppressionV3 = "NonMaxSuppressionV3"; +var NonMaxSuppressionV4 = "NonMaxSuppressionV4"; +var NonMaxSuppressionV5 = "NonMaxSuppressionV5"; +var OnesLike = "OnesLike"; +var OneHot = "OneHot"; +var Pack = "Pack"; +var PadV2 = "PadV2"; +var Pool = "Pool"; +var Pow = "Pow"; +var Prelu = "Prelu"; +var Prod = "Prod"; +var Range = "Range"; +var Real = "Real"; +var Reciprocal = "Reciprocal"; +var Relu = "Relu"; +var Reshape = "Reshape"; +var ResizeNearestNeighbor = "ResizeNearestNeighbor"; +var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; +var ResizeBilinear = "ResizeBilinear"; +var ResizeBilinearGrad = "ResizeBilinearGrad"; +var Relu6 = "Relu6"; +var Reverse = "Reverse"; +var Round = "Round"; +var Rsqrt = "Rsqrt"; +var ScatterNd = "ScatterNd"; +var Select = "Select"; +var Selu = "Selu"; +var Slice = "Slice"; +var Sin = "Sin"; +var Sinh = "Sinh"; +var Sign = "Sign"; +var Sigmoid = "Sigmoid"; +var Softplus = "Softplus"; +var Sqrt = "Sqrt"; +var Sum = "Sum"; +var SpaceToBatchND = "SpaceToBatchND"; +var SplitV = "SplitV"; +var Softmax = "Softmax"; +var SparseFillEmptyRows = "SparseFillEmptyRows"; +var SparseReshape = "SparseReshape"; +var SparseSegmentMean = "SparseSegmentMean"; +var SparseSegmentSum = "SparseSegmentSum"; +var SparseToDense = "SparseToDense"; +var SquaredDifference = "SquaredDifference"; +var Square = "Square"; +var StridedSlice = "StridedSlice"; +var StringNGrams = "StringNGrams"; +var StringSplit = "StringSplit"; +var StringToHashBucketFast = "StringToHashBucketFast"; +var Sub = "Sub"; +var Tan = "Tan"; +var Tanh = "Tanh"; +var Tile = "Tile"; +var TopK = "TopK"; +var Transform = "Transform"; +var Transpose = "Transpose"; +var Unique = "Unique"; +var Unpack = "Unpack"; +var UnsortedSegmentSum = "UnsortedSegmentSum"; +var ZerosLike = "ZerosLike"; +var Step = "Step"; +var FromPixels = "FromPixels"; +var RotateWithOffset = "RotateWithOffset"; +var _FusedMatMul = "_FusedMatMul"; +var FusedConv2D = "FusedConv2D"; +var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; +var kernelRegistry = getGlobal("kernelRegistry", () => new Map()); +var gradRegistry = getGlobal("gradRegistry", () => new Map()); +function getKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); +} +function getGradient(kernelName) { + return gradRegistry.get(kernelName); +} +function getKernelsForBackend(backendName) { + const it = kernelRegistry.entries(); + const result = []; + while (true) { + const { done, value } = it.next(); + if (done) { + break; + } + const [key, config3] = value; + const [backend22] = key.split("_"); + if (backend22 === backendName) { + result.push(config3); + } + } + return result; +} +function registerKernel(config3) { + const { kernelName, backendName } = config3; + const key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`); + } + kernelRegistry.set(key, config3); +} +function registerGradient(config3) { + const { kernelName } = config3; + if (gradRegistry.has(kernelName)) { + if (env().getBool("DEBUG")) { + console.warn(`Overriding the gradient for '${kernelName}'`); + } + } + gradRegistry.set(kernelName, config3); +} +function unregisterKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`); + } + kernelRegistry.delete(key); +} +function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error(`The gradient '${kernelName}' for backend is not registered`); + } + gradRegistry.delete(kernelName); +} +function copyRegisteredKernels(registeredBackendName, newBackendName) { + const kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach((kernelConfig) => { + const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName }); + registerKernel(newKernelConfig); + }); +} +function makeKey(kernelName, backendName) { + return `${backendName}_${kernelName}`; +} +var util_exports = {}; +__export2(util_exports, { + arraysEqual: () => arraysEqual, + assert: () => assert, + assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions, + assertNonNull: () => assertNonNull, + assertShapesMatch: () => assertShapesMatch, + bytesFromStringArray: () => bytesFromStringArray, + bytesPerElement: () => bytesPerElement, + checkConversionForErrors: () => checkConversionForErrors, + clamp: () => clamp, + computeStrides: () => computeStrides, + createScalarValue: () => createScalarValue, + createShuffledIndices: () => createShuffledIndices, + decodeString: () => decodeString, + distSquared: () => distSquared, + encodeString: () => encodeString, + fetch: () => fetch3, + fingerPrint64: () => fingerPrint64, + flatten: () => flatten, + getArrayFromDType: () => getArrayFromDType, + getTypedArrayFromDType: () => getTypedArrayFromDType, + hasEncodingLoss: () => hasEncodingLoss, + hexToLong: () => hexToLong, + indexToLoc: () => indexToLoc, + inferDtype: () => inferDtype, + inferFromImplicitShape: () => inferFromImplicitShape, + isBoolean: () => isBoolean, + isFunction: () => isFunction, + isInt: () => isInt, + isNumber: () => isNumber, + isPromise: () => isPromise, + isScalarShape: () => isScalarShape, + isString: () => isString, + isTypedArray: () => isTypedArray, + isValidDtype: () => isValidDtype, + locToIndex: () => locToIndex, + makeOnesTypedArray: () => makeOnesTypedArray, + makeZerosNestedTypedArray: () => makeZerosNestedTypedArray, + makeZerosTypedArray: () => makeZerosTypedArray, + nearestDivisor: () => nearestDivisor, + nearestLargerEven: () => nearestLargerEven, + now: () => now2, + parseAxisParam: () => parseAxisParam, + randUniform: () => randUniform, + repeatedTry: () => repeatedTry, + rightPad: () => rightPad, + shuffle: () => shuffle, + shuffleCombo: () => shuffleCombo, + sizeFromShape: () => sizeFromShape, + sizeToSquarishShape: () => sizeToSquarishShape, + squeezeShape: () => squeezeShape, + sum: () => sum, + swap: () => swap, + tanh: () => tanh, + toNestedArray: () => toNestedArray, + toTypedArray: () => toTypedArray +}); +var LongExports = __toModule(require_long()); +var Long = LongExports.default || LongExports; +function hexToLong(hex) { + return Long.fromString(hex, true, 16); +} +var k0 = hexToLong("c3a5c85c97cb3127"); +var k1 = hexToLong("b492b66fbe98f273"); +var k2 = hexToLong("9ae16a3b2f90404f"); +function shiftMix(val) { + return val.xor(val.shru(47)); +} +function fetch2(s, offset, numBytes) { + const bytes = s.slice(offset, offset + numBytes); + return Long.fromBytes(Array.from(bytes), true, true); +} +function fetch64(s, offset) { + return fetch2(s, offset, 8); +} +function fetch32(s, offset) { + return fetch2(s, offset, 4); +} +function rotate64(val, shift) { + return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift)); +} +function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) { + let a = u.xor(v).mul(mul2); + a = a.xor(a.shru(47)); + let b = v.xor(a).mul(mul2); + b = b.xor(b.shru(47)); + b = b.mul(mul2); + return b; +} +function weakHashLen32WithSeeds(w, x, y, z, a, b) { + a = a.add(w); + b = rotate64(b.add(a).add(z), 21); + const c = a; + a = a.add(x); + a = a.add(y); + b = b.add(rotate64(a, 44)); + return [a.add(z), b.add(c)]; +} +function weakHashLen32WithSeedsStr(s, offset, a, b) { + return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b); +} +function hashLen0to16(s, len = s.length) { + if (len >= 8) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).add(k2); + const b = fetch64(s, len - 8); + const c = rotate64(b, 37).mul(mul2).add(a); + const d = rotate64(a, 25).add(b).mul(mul2); + return hashLen16(c, d, mul2); + } + if (len >= 4) { + const mul2 = k2.add(len * 2); + const a = fetch32(s, 0); + return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2); + } + if (len > 0) { + const a = s[0]; + const b = s[len >> 1]; + const c = s[len - 1]; + const y = a + (b << 8); + const z = len + (c << 2); + return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2); + } + return k2; +} +function hashLen17to32(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k1); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2); +} +function hashLen33to64(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k2); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d); + const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2); + const e = fetch64(s, 16).mul(mul2); + const f = fetch64(s, 24); + const g = y.add(fetch64(s, len - 32)).mul(mul2); + const h = z.add(fetch64(s, len - 24)).mul(mul2); + return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2); +} +function fingerPrint64(s, len = s.length) { + const seed = Long.fromNumber(81, true); + if (len <= 32) { + if (len <= 16) { + return hashLen0to16(s, len); + } else { + return hashLen17to32(s, len); + } + } else if (len <= 64) { + return hashLen33to64(s, len); + } + let x = seed; + let y = seed.mul(k1).add(113); + let z = shiftMix(y.mul(k2).add(113)).mul(k2); + let v = [Long.UZERO, Long.UZERO]; + let w = [Long.UZERO, Long.UZERO]; + x = x.mul(k2).add(fetch64(s, 0)); + let offset = 0; + const end = (len - 1 >> 6) * 64; + const last64 = end + (len - 1 & 63) - 63; + do { + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1); + x = x.xor(w[1]); + y = y.add(v[0]).add(fetch64(s, offset + 40)); + z = rotate64(z.add(w[0]), 33).mul(k1); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + offset += 64; + } while (offset !== end); + const mul2 = k1.add(z.and(255).shl(1)); + offset = last64; + w[0] = w[0].add(len - 1 & 63); + v[0] = v[0].add(w[0]); + w[0] = w[0].add(v[0]); + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2); + x = x.xor(w[1].mul(9)); + y = y.add(v[0].mul(9).add(fetch64(s, offset + 40))); + z = rotate64(z.add(w[0]), 33).mul(mul2); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2); +} +function createScalarValue(value, dtype) { + if (dtype === "string") { + return encodeString(value); + } + return toTypedArray([value], dtype); +} +function noConversionNeeded(a, dtype) { + return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool"; +} +function toTypedArray(a, dtype) { + if (dtype === "string") { + throw new Error("Cannot convert a string[] to a TypedArray"); + } + if (Array.isArray(a)) { + a = flatten(a); + } + if (env().getBool("DEBUG")) { + checkConversionForErrors(a, dtype); + } + if (noConversionNeeded(a, dtype)) { + return a; + } + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(a); + } else if (dtype === "int32") { + return new Int32Array(a); + } else if (dtype === "bool") { + const bool = new Uint8Array(a.length); + for (let i = 0; i < bool.length; ++i) { + if (Math.round(a[i]) !== 0) { + bool[i] = 1; + } + } + return bool; + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function now2() { + return env().platform.now(); +} +function fetch3(path, requestInits) { + return env().platform.fetch(path, requestInits); +} +function encodeString(s, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.encode(s, encoding); +} +function decodeString(bytes, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.decode(bytes, encoding); +} +var Profiler = class { + constructor(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + profileKernel(kernelName, inputs, f) { + let outputs; + const holdResultWrapperFn = () => { + outputs = f(); + }; + let timer; + const start = now2(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } else { + holdResultWrapperFn(); + for (const output of outputs) { + output.dataSync(); + } + timer = Promise.resolve({ kernelMs: now2() - start }); + } + if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) { + for (let i = 0; i < outputs.length; i++) { + const output = outputs[i]; + output.data().then((tensorVals) => { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + } + } + const kernelProfile = { + kernelName, + outputs, + inputs, + timeMs: timer.then((timing) => timing.kernelMs), + extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "") + }; + return kernelProfile; + } + logKernelProfile(kernelProfile) { + const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile; + outputs.forEach((result) => { + Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => { + this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + } +}; +function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== "float32") { + return false; + } + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + console.warn(`Found ${num} in the result of '${kernelName}'`); + return true; + } + } + return false; +} +var Logger = class { + logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { + const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"]; + const paddedName = rightPad(name, 25); + const rank = result.rank; + const size = result.size; + const shape = rightPad(result.shape.toString(), 14); + let inputShapesDescription = ""; + for (const name2 in inputs) { + const input2 = inputs[name2]; + if (input2 != null) { + const inputShape = input2.shape || result.shape; + const inputRank = inputShape.length; + inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `; + } + } + console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); + } +}; +function getFilteredNodesXToY(tape, xs, y) { + const tensorsFromX = {}; + const nodesFromX = {}; + for (let i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (const inputName in nodeInputs) { + const input2 = nodeInputs[inputName]; + let anyInputFromX = false; + for (let j = 0; j < xs.length; j++) { + if (tensorsFromX[input2.id]) { + node.outputs.forEach((output) => tensorsFromX[output.id] = true); + anyInputFromX = true; + nodesFromX[node.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + const tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + const nodesToY = {}; + for (let i = tape.length - 1; i >= 0; i--) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (let j = 0; j < node.outputs.length; j++) { + if (tensorsLeadToY[node.outputs[j].id]) { + for (const inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node.id] = true; + } + break; + } + } + } + const filteredTape = []; + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + if (nodesFromX[node.id] && nodesToY[node.id]) { + const prunedInputs = {}; + for (const inputName in node.inputs) { + const nodeInput = node.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + const prunedNode = Object.assign({}, node); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; +} +function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) { + for (let i = filteredTape.length - 1; i >= 0; i--) { + const node = filteredTape[i]; + const dys = []; + node.outputs.forEach((o) => { + const gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } else { + dys.push(null); + } + }); + if (node.gradient == null) { + throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`); + } + const inputGradients = node.gradient(dys); + for (const inputName in node.inputs) { + if (!(inputName in inputGradients)) { + throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`); + } + const dx = tidy2(() => inputGradients[inputName]()); + if (dx.dtype !== "float32") { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); + } + const x = node.inputs[inputName]; + if (!arraysEqual(dx.shape, x.shape)) { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`); + } + if (tensorAccumulatedGradientMap[x.id] == null) { + tensorAccumulatedGradientMap[x.id] = dx; + } else { + const curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx); + curGradient.dispose(); + } + } + } +} +var FORMAT_LIMIT_NUM_VALS = 20; +var FORMAT_NUM_FIRST_LAST_VALS = 3; +var FORMAT_NUM_SIG_DIGITS = 7; +function tensorToString(vals, shape, dtype, verbose) { + const strides = computeStrides(shape); + const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + const rank = shape.length; + const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + const lines2 = ["Tensor"]; + if (verbose) { + lines2.push(` dtype: ${dtype}`); + lines2.push(` rank: ${rank}`); + lines2.push(` shape: [${shape}]`); + lines2.push(` values:`); + } + lines2.push(valsLines.map((l) => " " + l).join("\n")); + return lines2.join("\n"); +} +function computeMaxSizePerColumn(vals, shape, dtype, strides) { + const n = sizeFromShape(shape); + const numCols = strides[strides.length - 1]; + const padPerCol = new Array(numCols).fill(0); + const rank = shape.length; + const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (let row = 0; row < n / numCols; row++) { + const offset = row * numCols; + for (let j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; +} +function valToString(val, pad3, dtype) { + let valStr; + if (Array.isArray(val)) { + valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; + } else if (isString(val)) { + valStr = `'${val}'`; + } else if (dtype === "bool") { + valStr = boolNumToString(val); + } else { + valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); + } + return rightPad(valStr, pad3); +} +function boolNumToString(v) { + return v === 0 ? "false" : "true"; +} +function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) { + const storagePerElement = dtype === "complex64" ? 2 : 1; + const size = shape[0]; + const rank = shape.length; + if (rank === 0) { + if (dtype === "complex64") { + const complexTuple = createComplexTuples(vals); + return [valToString(complexTuple[0], 0, dtype)]; + } + if (dtype === "bool") { + return [boolNumToString(vals[0])]; + } + return [vals[0].toString()]; + } + if (rank === 1) { + if (size > FORMAT_LIMIT_NUM_VALS) { + const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + let firstVals = Array.from(vals.slice(0, firstValsSize)); + let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement)); + if (dtype === "complex64") { + firstVals = createComplexTuples(firstVals); + lastVals = createComplexTuples(lastVals); + } + return [ + "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]" + ]; + } + const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals); + return [ + "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]" + ]; + } + const subshape = shape.slice(1); + const substrides = strides.slice(1); + const stride = strides[0] * storagePerElement; + const lines2 = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false)); + } + lines2.push("..."); + for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1)); + } + } else { + for (let i = 0; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1)); + } + } + const sep = rank === 2 ? "," : ""; + lines2[0] = "[" + lines2[0] + sep; + for (let i = 1; i < lines2.length - 1; i++) { + lines2[i] = " " + lines2[i] + sep; + } + let newLineSep = ",\n"; + for (let i = 2; i < rank; i++) { + newLineSep += "\n"; + } + lines2[lines2.length - 1] = " " + lines2[lines2.length - 1] + "]" + (isLast ? "" : newLineSep); + return lines2; +} +function createComplexTuples(vals) { + const complexTuples = []; + for (let i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; +} +var TensorBuffer = class { + constructor(shape, dtype, values) { + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + const n = values.length; + assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + } + if (dtype === "complex64") { + throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`); + } + this.values = values || getArrayFromDType(dtype, this.size); + this.strides = computeStrides(shape); + } + set(value, ...locs) { + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`); + const index = this.locToIndex(locs); + this.values[index] = value; + } + get(...locs) { + if (locs.length === 0) { + locs = [0]; + } + let i = 0; + for (const loc of locs) { + if (loc < 0 || loc >= this.shape[i]) { + const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`; + throw new Error(msg); + } + i++; + } + let index = locs[locs.length - 1]; + for (let i2 = 0; i2 < locs.length - 1; ++i2) { + index += this.strides[i2] * locs[i2]; + } + return this.values[index]; + } + locToIndex(locs) { + if (this.rank === 0) { + return 0; + } else if (this.rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + } + indexToLoc(index) { + if (this.rank === 0) { + return []; + } else if (this.rank === 1) { + return [index]; + } + const locs = new Array(this.shape.length); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / this.strides[i]); + index -= locs[i] * this.strides[i]; + } + locs[locs.length - 1] = index; + return locs; + } + get rank() { + return this.shape.length; + } + toTensor() { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + } +}; +var trackerFn = null; +var opHandler = null; +var deprecationWarningFn = null; +function setTensorTracker(fn) { + trackerFn = fn; +} +function setOpHandler(handler) { + opHandler = handler; +} +function setDeprecationWarningFn(fn) { + deprecationWarningFn = fn; +} +var Tensor = class { + constructor(shape, dtype, dataId, id) { + this.kept = false; + this.isDisposedInternal = false; + this.shape = shape.slice(); + this.dtype = dtype || "float32"; + this.size = sizeFromShape(shape); + this.strides = computeStrides(shape); + this.dataId = dataId; + this.id = id; + this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; + } + get rank() { + return this.shape.length; + } + async buffer() { + const vals = await this.data(); + return opHandler.buffer(this.shape, this.dtype, vals); + } + bufferSync() { + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); + } + async array() { + const vals = await this.data(); + return toNestedArray(this.shape, vals, this.dtype === "complex64"); + } + arraySync() { + return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64"); + } + async data() { + this.throwIfDisposed(); + const data = trackerFn().read(this.dataId); + if (this.dtype === "string") { + const bytes = await data; + try { + return bytes.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + dataSync() { + this.throwIfDisposed(); + const data = trackerFn().readSync(this.dataId); + if (this.dtype === "string") { + try { + return data.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + async bytes() { + this.throwIfDisposed(); + const data = await trackerFn().read(this.dataId); + if (this.dtype === "string") { + return data; + } else { + return new Uint8Array(data.buffer); + } + } + dispose() { + if (this.isDisposed) { + return; + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + } + get isDisposed() { + return this.isDisposedInternal; + } + throwIfDisposed() { + if (this.isDisposed) { + throw new Error(`Tensor is disposed.`); + } + } + print(verbose = false) { + return opHandler.print(this, verbose); + } + clone() { + this.throwIfDisposed(); + return opHandler.clone(this); + } + toString(verbose = false) { + const vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + } + cast(dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); + } + variable(trainable = true, name, dtype) { + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + } +}; +Object.defineProperty(Tensor, Symbol.hasInstance, { + value: (instance) => { + return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; + } +}); +function getGlobalTensorClass() { + return getGlobal("Tensor", () => { + return Tensor; + }); +} +getGlobalTensorClass(); +var Variable = class extends Tensor { + constructor(initialValue, trainable, name, tensorId) { + super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); + this.trainable = trainable; + this.name = name; + } + assign(newValue) { + if (newValue.dtype !== this.dtype) { + throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`); + } + if (!arraysEqual(newValue.shape, this.shape)) { + throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`); + } + trackerFn().disposeTensor(this); + this.dataId = newValue.dataId; + trackerFn().incRef(this, null); + } + dispose() { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + } +}; +Object.defineProperty(Variable, Symbol.hasInstance, { + value: (instance) => { + return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; + } +}); +var tensor_util_exports = {}; +__export2(tensor_util_exports, { + assertTypesMatch: () => assertTypesMatch, + getTensorsInContainer: () => getTensorsInContainer, + isTensorInList: () => isTensorInList, + makeTypesMatch: () => makeTypesMatch +}); +var Rank; +(function(Rank2) { + Rank2["R0"] = "R0"; + Rank2["R1"] = "R1"; + Rank2["R2"] = "R2"; + Rank2["R3"] = "R3"; + Rank2["R4"] = "R4"; + Rank2["R5"] = "R5"; + Rank2["R6"] = "R6"; +})(Rank || (Rank = {})); +var UpcastInt32AndMap; +(function(UpcastInt32AndMap2) { + UpcastInt32AndMap2["float32"] = "float32"; + UpcastInt32AndMap2["int32"] = "int32"; + UpcastInt32AndMap2["bool"] = "int32"; + UpcastInt32AndMap2["complex64"] = "complex64"; +})(UpcastInt32AndMap || (UpcastInt32AndMap = {})); +var UpcastBoolAndMap; +(function(UpcastBoolAndMap2) { + UpcastBoolAndMap2["float32"] = "float32"; + UpcastBoolAndMap2["int32"] = "int32"; + UpcastBoolAndMap2["bool"] = "bool"; + UpcastBoolAndMap2["complex64"] = "complex64"; +})(UpcastBoolAndMap || (UpcastBoolAndMap = {})); +var UpcastFloat32AndMap; +(function(UpcastFloat32AndMap2) { + UpcastFloat32AndMap2["float32"] = "float32"; + UpcastFloat32AndMap2["int32"] = "float32"; + UpcastFloat32AndMap2["bool"] = "float32"; + UpcastFloat32AndMap2["complex64"] = "complex64"; +})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); +var UpcastComplex64AndMap; +(function(UpcastComplex64AndMap2) { + UpcastComplex64AndMap2["float32"] = "complex64"; + UpcastComplex64AndMap2["int32"] = "complex64"; + UpcastComplex64AndMap2["bool"] = "complex64"; + UpcastComplex64AndMap2["complex64"] = "complex64"; +})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {})); +var upcastTypeMap = { + "float32": UpcastFloat32AndMap, + "int32": UpcastInt32AndMap, + "bool": UpcastBoolAndMap, + "complex64": UpcastComplex64AndMap +}; +function upcastType(typeA, typeB) { + if (typeA === "string" || typeB === "string") { + if (typeA === "string" && typeB === "string") { + return "string"; + } + throw new Error(`Can not upcast ${typeA} with ${typeB}`); + } + return upcastTypeMap[typeA][typeB]; +} +function sumOutType(type) { + return upcastType(type, "int32"); +} +function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + const dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; +} +function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`); +} +function isTensorInList(tensor2, tensorList) { + return tensorList.some((x) => x.id === tensor2.id); +} +function getTensorsInContainer(result) { + const list = []; + const seen = new Set(); + walkTensorContainer(result, list, seen); + return list; +} +function walkTensorContainer(container, list, seen) { + if (container == null) { + return; + } + if (container instanceof Tensor) { + list.push(container); + return; + } + if (!isIterable(container)) { + return; + } + const iterable = container; + for (const k in iterable) { + const val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } +} +function isIterable(obj) { + return Array.isArray(obj) || typeof obj === "object"; +} +function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; +} +var EngineState = 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 = false; + this.activeProfile = { + newBytes: 0, + newTensors: 0, + peakBytes: 0, + kernels: [], + result: null, + get kernelNames() { + return Array.from(new Set(this.kernels.map((k) => k.name))); + } + }; + } + dispose() { + for (const variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + } +}; +var Engine = class { + constructor(ENV5) { + this.ENV = ENV5; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + async ready() { + if (this.pendingBackendInit != null) { + return this.pendingBackendInit.then(() => { + }); + } + if (this.backendInstance != null) { + return; + } + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const success = await this.initializeBackend(backendName).success; + if (success) { + await this.setBackend(backendName); + return; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + get backend() { + if (this.pendingBackendInit != null) { + throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + if (this.backendInstance == null) { + const { name, asyncInit } = this.initializeBackendsAndReturnBest(); + if (asyncInit) { + throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + this.setBackend(name); + } + return this.backendInstance; + } + backendNames() { + return Object.keys(this.registryFactory); + } + findBackend(backendName) { + if (!(backendName in this.registry)) { + if (backendName in this.registryFactory) { + const { asyncInit } = this.initializeBackend(backendName); + if (asyncInit) { + return null; + } + } else { + return null; + } + } + return this.registry[backendName]; + } + findBackendFactory(backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + } + registerBackend(backendName, factory, priority = 1) { + if (backendName in this.registryFactory) { + console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`); + return false; + } + this.registryFactory[backendName] = { factory, priority }; + return true; + } + async setBackend(backendName) { + if (this.registryFactory[backendName] == null) { + throw new Error(`Backend name '${backendName}' not found in registry`); + } + this.backendName = backendName; + if (this.registry[backendName] == null) { + this.backendInstance = null; + const { success, asyncInit } = this.initializeBackend(backendName); + const result = asyncInit ? await success : success; + if (!result) { + return false; + } + } + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + this.profiler = new Profiler(this.backendInstance); + return true; + } + setupRegisteredKernels() { + const kernels = getKernelsForBackend(this.backendName); + kernels.forEach((kernel) => { + if (kernel.setupFunc != null) { + kernel.setupFunc(this.backendInstance); + } + }); + } + disposeRegisteredKernels(backendName) { + const kernels = getKernelsForBackend(backendName); + kernels.forEach((kernel) => { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(this.registry[backendName]); + } + }); + } + initializeBackend(backendName) { + const registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); + } + try { + const backend22 = registryFactoryEntry.factory(); + if (backend22 && !(backend22 instanceof KernelBackend) && typeof backend22.then === "function") { + const promiseId = ++this.pendingBackendInitId; + const success = backend22.then((backendInstance) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.registry[backendName] = backendInstance; + this.pendingBackendInit = null; + return true; + }).catch((err) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.pendingBackendInit = null; + console.warn(`Initialization of backend ${backendName} failed`); + console.warn(err.stack || err.message); + return false; + }); + this.pendingBackendInit = success; + return { success, asyncInit: true }; + } else { + this.registry[backendName] = backend22; + return { success: true, asyncInit: false }; + } + } catch (err) { + console.warn(`Initialization of backend ${backendName} failed`); + console.warn(err.stack || err.message); + return { success: false, asyncInit: false }; + } + } + removeBackend(backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(`${backendName} backend not found in registry`); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + } + getSortedBackends() { + if (Object.keys(this.registryFactory).length === 0) { + throw new Error("No backend found in registry."); + } + return Object.keys(this.registryFactory).sort((a, b) => { + return this.registryFactory[b].priority - this.registryFactory[a].priority; + }); + } + initializeBackendsAndReturnBest() { + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const { success, asyncInit } = this.initializeBackend(backendName); + if (asyncInit || success) { + return { name: backendName, asyncInit }; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + moveData(backend22, dataId) { + const info2 = this.state.tensorInfo.get(dataId); + const srcBackend = info2.backend; + const values = this.readSync(dataId); + const refCount = srcBackend.refCount(dataId); + srcBackend.disposeData(dataId, true); + info2.backend = backend22; + backend22.move(dataId, values, info2.shape, info2.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + } + tidy(nameOrFn, fn) { + let name = null; + if (fn == null) { + if (typeof nameOrFn !== "function") { + throw new Error("Please provide a function to tidy()"); + } + fn = nameOrFn; + } else { + if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) { + throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); + } + if (typeof fn !== "function") { + throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); + } + name = nameOrFn; + } + let result; + return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { + result = fn(); + if (result instanceof Promise) { + console.error("Cannot return a Promise inside of tidy."); + } + return result; + }); + } + scopedRun(start, end, f) { + start(); + try { + const res = f(); + end(); + return res; + } catch (ex) { + end(); + throw ex; + } + } + nextTensorId() { + return Engine.nextTensorId++; + } + nextVariableId() { + return Engine.nextVariableId++; + } + clone(x) { + const y = ENGINE.runKernel(Identity, { x }); + const inputs = { x }; + const grad2 = (dy) => ({ + x: () => { + const dtype = "float32"; + const gradInputs = { x: dy }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, gradInputs, attrs); + } + }); + const saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {}); + return y; + } + runKernel(kernelName, inputs, attrs) { + if (this.backendName == null) { + this.backend; + } + const hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); + } + return this.runKernelFunc({ kernelName, inputs, attrs }); + } + shouldCheckForMemLeaks() { + return this.ENV.getBool("IS_TEST"); + } + checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { + const numDataIdsAfter = this.backend.numDataIds(); + let numOutputDataIds = 0; + outInfos.forEach((info2) => { + numOutputDataIds += info2.dtype === "complex64" ? 3 : 1; + }); + const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; + if (dataIdsLeaked > 0) { + throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`); + } + } + runKernelFunc(kernelParams) { + let outputs; + let saved = []; + const isTapeOn = this.isTapeOn(); + const startingBytecount = this.state.numBytes; + const startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + let kernelFunc3; + if (this.backendName == null) { + this.backend; + } + let out; + const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (isRegisteredKernelInvocation(kernelParams)) { + const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams; + if (this.backendName == null) { + this.backend; + } + const kernel = getKernel(kernelName, this.backendName); + assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend }); + const outInfos = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); + } + const outTensors = outInfos.map((outInfo) => { + if (outInfo.rank != null) { + return outInfo; + } + const { dataId, shape, dtype } = outInfo; + return this.makeTensorFromDataId(dataId, shape, dtype); + }); + if (isTapeOn) { + const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors); + saved = this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } else { + const { forwardFunc } = kernelParams; + const saveFunc = (tensors) => { + if (!isTapeOn) { + return; + } + saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + }; + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = this.tidy(() => forwardFunc(this.backend, saveFunc)); + const outs = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + const { inputs, attrs } = kernelParams; + const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; + let kernelProfile; + this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { + if (!this.ENV.getBool("DEBUG") && !this.state.profiling) { + outputs = kernelFunc3(); + } else { + kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3()); + if (this.ENV.getBool("DEBUG")) { + this.profiler.logKernelProfile(kernelProfile); + } + outputs = kernelProfile.outputs; + } + }); + if (isTapeOn) { + this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); + } + if (this.state.profiling) { + this.state.activeProfile.kernels.push({ + name: kernelOrScopeName, + bytesAdded: this.state.numBytes - startingBytecount, + totalBytesSnapshot: this.state.numBytes, + tensorsAdded: this.state.numTensors - startingNumTensors, + totalTensorsSnapshot: this.state.numTensors, + inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null), + outputShapes: outputs.map((item) => item.shape), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return Array.isArray(out) ? outputs : outputs[0]; + } + saveTensorsForBackwardMode(tensors) { + const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + return saved; + } + getTensorsForGradient(kernelName, inputs, outputs) { + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + const inputsToSave = gradConfig.inputsToSave || []; + const outputsToSave = gradConfig.outputsToSave || []; + let inputTensorsToSave; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array."); + inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); + } else { + inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); + } + const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); + return inputTensorsToSave.concat(outputTensorsToSave); + } + return []; + } + makeTensor(values, shape, dtype, backend22) { + if (values == null) { + throw new Error("Values passed to engine.makeTensor() are null"); + } + dtype = dtype || "float32"; + backend22 = backend22 || this.backend; + let backendVals = values; + if (dtype === "string" && isString(values[0])) { + backendVals = values.map((d) => encodeString(d)); + } + const dataId = backend22.write(backendVals, shape, dtype); + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend22); + if (dtype === "string") { + const info2 = this.state.tensorInfo.get(dataId); + const newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info2.bytes; + info2.bytes = newBytes; + } + return t; + } + makeTensorFromDataId(dataId, shape, dtype, backend22) { + dtype = dtype || "float32"; + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend22); + return t; + } + makeVariable(initialValue, trainable = true, name, dtype) { + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + const v = new Variable(initialValue, trainable, name, this.nextTensorId()); + if (this.state.registeredVariables[v.name] != null) { + throw new Error(`Variable with name ${v.name} was already registered`); + } + this.state.registeredVariables[v.name] = v; + this.incRef(v, this.backend); + return v; + } + trackTensor(a, backend22) { + this.state.numTensors++; + if (a.dtype === "string") { + this.state.numStringTensors++; + } + let bytes = 0; + if (a.dtype !== "complex64" && a.dtype !== "string") { + bytes = a.size * bytesPerElement(a.dtype); + } + this.state.numBytes += bytes; + if (!this.state.tensorInfo.has(a.dataId)) { + this.state.numDataBuffers++; + this.state.tensorInfo.set(a.dataId, { + backend: backend22 || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + } + incRef(a, backend22) { + this.trackTensor(a, backend22); + this.backend.incRef(a.dataId); + } + removeDataId(dataId, backend22) { + if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend22) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + } + disposeTensor(a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + const info2 = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === "string") { + this.state.numStringTensors--; + this.state.numBytes -= info2.bytes; + } + if (a.dtype !== "complex64" && a.dtype !== "string") { + const bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + if (info2.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info2.backend); + } + } + disposeVariables() { + for (const varName in this.state.registeredVariables) { + const v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + } + disposeVariable(v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + } + memory() { + const info2 = this.backend.memory(); + info2.numTensors = this.state.numTensors; + info2.numDataBuffers = this.state.numDataBuffers; + info2.numBytes = this.state.numBytes; + if (this.state.numStringTensors > 0) { + info2.unreliable = true; + if (info2.reasons == null) { + info2.reasons = []; + } + info2.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)"); + } + return info2; + } + async profile(query) { + this.state.profiling = true; + const startBytes = this.state.numBytes; + const startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + this.state.activeProfile.result = await query(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot)); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; + for (const kernel of this.state.activeProfile.kernels) { + kernel.kernelTimeMs = await kernel.kernelTimeMs; + kernel.extraInfo = await kernel.extraInfo; + } + return this.state.activeProfile; + } + isTapeOn() { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + } + addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved }; + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = (dys) => { + dys = dys.map((dy, i) => { + if (dy == null) { + const output = outputs[i]; + const vals = makeZerosTypedArray(output.size, output.dtype); + return this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + } + keep(result) { + result.kept = true; + return result; + } + startTape() { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + } + endTape() { + this.state.gradientDepth--; + } + startScope(name) { + const scopeInfo = { + track: [], + name: "unnamed scope", + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + } + endScope(result) { + const tensorsToTrackInParent = getTensorsInContainer(result); + const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id)); + for (let i = 0; i < this.state.activeScope.track.length; i++) { + const tensor2 = this.state.activeScope.track[i]; + if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) { + tensor2.dispose(); + } + } + const oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; + tensorsToTrackInParent.forEach((tensor2) => { + if (!tensor2.kept && tensor2.scopeId === oldScope.id) { + this.track(tensor2); + } + }); + } + gradients(f, xs, dy, allowNoGradients = false) { + assert(xs.length > 0, () => "gradients() received an empty list of xs."); + if (dy != null && dy.dtype !== "float32") { + throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); + } + const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f)); + assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor."); + const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); + if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) { + throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); + } + return this.tidy("backward", () => { + const accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy; + backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add); + const grads2 = xs.map((x) => accumulatedGradientMap[x.id]); + if (this.state.gradientDepth === 0) { + this.state.activeTape.forEach((node) => { + for (const tensor2 of node.saved) { + tensor2.dispose(); + } + }); + this.state.activeTape = null; + } + return { value: y, grads: grads2 }; + }); + } + customGrad(f) { + assert(isFunction(f), () => "The f passed in customGrad(f) must be a function."); + return (...inputs) => { + assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + let res; + const inputMap = {}; + inputs.forEach((input2, i) => { + inputMap[i] = input2; + }); + const forwardFunc = (_, save) => { + res = f(...[...inputs, save]); + assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"); + assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."); + return res.value; + }; + const backwardsFunc = (dy, saved) => { + const gradRes = res.gradFunc(dy, saved); + const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."); + assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + const gradMap = {}; + grads2.forEach((grad2, i) => { + gradMap[i] = () => grad2; + }); + return gradMap; + }; + return this.runKernelFunc({ + forwardFunc, + backwardsFunc, + inputs: inputMap + }); + }; + } + readSync(dataId) { + const info2 = this.state.tensorInfo.get(dataId); + return info2.backend.readSync(dataId); + } + read(dataId) { + const info2 = this.state.tensorInfo.get(dataId); + return info2.backend.read(dataId); + } + async time(query) { + const start = now2(); + const timingInfo = await this.backend.time(query); + timingInfo.wallMs = now2() - start; + return timingInfo; + } + track(result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + } + get registeredVariables() { + return this.state.registeredVariables; + } + reset() { + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (const backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + } +}; +Engine.nextTensorId = 0; +Engine.nextVariableId = 0; +function ones(shape) { + const values = makeOnesTypedArray(sizeFromShape(shape), "float32"); + return ENGINE.makeTensor(values, shape, "float32"); +} +function getOrMakeEngine() { + const ns = getGlobalNamespace(); + if (ns._tfengine == null) { + const environment = new Environment(ns); + ns._tfengine = new Engine(environment); + } + setEnvironmentGlobal(ns._tfengine.ENV); + setTensorTracker(() => ns._tfengine); + return ns._tfengine; +} +var ENGINE = getOrMakeEngine(); +function add(a, b) { + const inputs = { a, b }; + return ENGINE.runKernel(Add, inputs); +} +var device_util_exports = {}; +__export2(device_util_exports, { + isBrowser: () => isBrowser, + isMobile: () => isMobile +}); +function _isNavigatorDefined() { + return typeof navigator !== "undefined" && navigator != null; +} +function isMobile(nav) { + if (nav || _isNavigatorDefined()) { + if (!nav) { + nav = navigator; + } + if (nav.product === "ReactNative") { + return true; + } + const a = nav.userAgent || nav.vendor || window.opera; + return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4)); + } + return false; +} +function isBrowser() { + return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined"; +} +var ENV2 = env(); +ENV2.registerFlag("DEBUG", () => false, (debugValue) => { + if (debugValue) { + console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); + } +}); +ENV2.registerFlag("IS_BROWSER", () => isBrowser()); +ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"); +ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +ENV2.registerFlag("PROD", () => false); +ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +ENV2.registerFlag("IS_TEST", () => false); +ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true); +ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +function inferShape(val, dtype) { + let firstElem = val; + if (isTypedArray(val)) { + return dtype === "string" ? [] : [val.length]; + } + if (!Array.isArray(val)) { + return []; + } + const shape = []; + while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") { + shape.push(firstElem.length); + firstElem = firstElem[0]; + } + if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) { + deepAssertShapeConsistency(val, shape, []); + } + return shape; +} +function deepAssertShapeConsistency(val, shape, indices) { + indices = indices || []; + if (!Array.isArray(val) && !isTypedArray(val)) { + assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`); + return; + } + assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`); + assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`); + const subShape = shape.slice(1); + for (let i = 0; i < val.length; ++i) { + deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); + } +} +function assertDtype(expectedDtype, actualDType, argName, functionName) { + if (expectedDtype === "string_or_numeric") { + return; + } + if (expectedDtype == null) { + throw new Error(`Expected dtype cannot be null.`); + } + if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") { + throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`); + } +} +function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") { + if (x instanceof Tensor) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + let inferredDtype = inferDtype(x); + if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) { + inferredDtype = parseAsDtype; + } + assertDtype(parseAsDtype, inferredDtype, argName, functionName); + if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") { + const type = x == null ? "null" : x.constructor.name; + throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`); + } + const inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + const skipTypedArray = true; + const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); +} +function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") { + if (!Array.isArray(arg)) { + throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``); + } + const tensors = arg; + return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); +} +var OP_SCOPE_SUFFIX = "__op"; +function op(f) { + const keys = Object.keys(f); + if (keys.length !== 1) { + throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`); + } + let opName = keys[0]; + const fn = f[opName]; + if (opName.endsWith("_")) { + opName = opName.substring(0, opName.length - 1); + } + opName = opName + OP_SCOPE_SUFFIX; + const f2 = (...args) => { + ENGINE.startScope(opName); + try { + const result = fn(...args); + if (isPromise(result)) { + console.error("Cannot return a Promise inside of tidy."); + } + ENGINE.endScope(result); + return result; + } catch (ex) { + ENGINE.endScope(null); + throw ex; + } + }; + Object.defineProperty(f2, "name", { value: opName, configurable: true }); + return f2; +} +function complex_(real4, imag4) { + const $real = convertToTensor(real4, "real", "complex"); + const $imag = convertToTensor(imag4, "imag", "complex"); + assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); + const inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); +} +var complex = op({ complex_ }); +function makeTensor(values, shape, inferredShape, dtype) { + if (dtype == null) { + dtype = inferDtype(values); + } + if (dtype === "complex64") { + throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`); + } + if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") { + throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); + } + if (shape != null) { + assertNonNegativeIntegerDimensions(shape); + const providedSize = sizeFromShape(shape); + const inferredSize = sizeFromShape(inferredShape); + assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`); + for (let i = 0; i < inferredShape.length; ++i) { + const inferred = inferredShape[i]; + const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `); + } + } + if (!isTypedArray(values) && !Array.isArray(values)) { + values = [values]; + } + shape = shape || inferredShape; + values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true); + return ENGINE.makeTensor(values, shape, dtype); +} +function tensor(values, shape, dtype) { + const inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); +} +var DTYPE_VALUE_SIZE_MAP = { + "float32": 4, + "float16": 2, + "int32": 4, + "uint16": 2, + "uint8": 1, + "bool": 1, + "complex64": 8 +}; +var NUM_BYTES_STRING_LENGTH = 4; +async function encodeWeights(tensors, group) { + const specs = []; + const dataPromises = []; + const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors); + for (let i = 0; i < names.length; ++i) { + const name = names[i]; + const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name]; + if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") { + throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`); + } + const spec = { name, shape: t.shape, dtype: t.dtype }; + if (t.dtype === "string") { + const utf8bytes = new Promise(async (resolve) => { + const vals = await t.bytes(); + const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length; + const bytes = new Uint8Array(totalNumBytes); + let offset = 0; + for (let i2 = 0; i2 < vals.length; i2++) { + const val = vals[i2]; + const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer); + bytes.set(bytesOfLength, offset); + offset += NUM_BYTES_STRING_LENGTH; + bytes.set(val, offset); + offset += val.length; + } + resolve(bytes); + }); + dataPromises.push(utf8bytes); + } else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + } + const tensorValues = await Promise.all(dataPromises); + return { data: concatenateTypedArrays(tensorValues), specs }; +} +function decodeWeights(buffer2, specs) { + const out = {}; + let float16Decode; + let offset = 0; + for (const spec of specs) { + const name = spec.name; + const dtype = spec.dtype; + const shape = spec.shape; + const size = sizeFromShape(shape); + let values; + if ("quantization" in spec) { + const quantization = spec.quantization; + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + if (!("min" in quantization && "scale" in quantization)) { + throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`); + } + } else if (quantization.dtype === "float16") { + if (dtype !== "float32") { + throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`); + } + } else { + throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + } + const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor); + const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); + if (dtype === "float32") { + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + values = new Float32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } else if (quantization.dtype === "float16") { + if (float16Decode === void 0) { + float16Decode = getFloat16Decoder(); + } + values = float16Decode(quantizedArray); + } else { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`); + } + } else if (dtype === "int32") { + if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`); + } + values = new Int32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = Math.round(v * quantization.scale + quantization.min); + } + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * quantizationSizeFactor; + } else if (dtype === "string") { + const size2 = sizeFromShape(spec.shape); + values = []; + for (let i = 0; i < size2; i++) { + const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; + } + } else { + const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor); + if (dtype === "float32") { + values = new Float32Array(byteBuffer); + } else if (dtype === "int32") { + values = new Int32Array(byteBuffer); + } else if (dtype === "bool") { + values = new Uint8Array(byteBuffer); + } else if (dtype === "complex64") { + values = new Float32Array(byteBuffer); + const real4 = new Float32Array(values.length / 2); + const image32 = new Float32Array(values.length / 2); + for (let i = 0; i < real4.length; i++) { + real4[i] = values[i * 2]; + image32[i] = values[i * 2 + 1]; + } + const realTensor = tensor(real4, shape, "float32"); + const imageTensor = tensor(image32, shape, "float32"); + out[name] = complex(realTensor, imageTensor); + realTensor.dispose(); + imageTensor.dispose(); + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * dtypeFactor; + } + if (dtype !== "complex64") { + out[name] = tensor(values, shape, dtype); + } + } + return out; +} +function concatenateTypedArrays(xs) { + if (xs === null) { + throw new Error(`Invalid input value: ${JSON.stringify(xs)}`); + } + let totalByteLength = 0; + const normalizedXs = []; + xs.forEach((x) => { + totalByteLength += x.byteLength; + normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x)); + if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) { + throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`); + } + }); + const y = new Uint8Array(totalByteLength); + let offset = 0; + normalizedXs.forEach((x) => { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; + }); + return y.buffer; +} +var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined"); +function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str); + } + return new Blob([str]).size; +} +function arrayBufferToBase64String(buffer2) { + if (useNodeBuffer) { + return Buffer.from(buffer2).toString("base64"); + } + const buf = new Uint8Array(buffer2); + let s = ""; + for (let i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); +} +function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + const buf = Buffer.from(str, "base64"); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + const s = atob(str); + const buffer2 = new Uint8Array(s.length); + for (let i = 0; i < s.length; ++i) { + buffer2.set([s.charCodeAt(i)], i); + } + return buffer2.buffer; +} +function concatenateArrayBuffers(buffers) { + if (buffers.length === 1) { + return buffers[0]; + } + let totalByteLength = 0; + buffers.forEach((buffer2) => { + totalByteLength += buffer2.byteLength; + }); + const temp = new Uint8Array(totalByteLength); + let offset = 0; + buffers.forEach((buffer2) => { + temp.set(new Uint8Array(buffer2), offset); + offset += buffer2.byteLength; + }); + return temp.buffer; +} +function basename(path) { + const SEPARATOR = "/"; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + const items = path.split(SEPARATOR); + return items[items.length - 1]; +} +function getModelJSONForModelArtifacts(artifacts, manifest) { + const result = { + modelTopology: artifacts.modelTopology, + format: artifacts.format, + generatedBy: artifacts.generatedBy, + convertedBy: artifacts.convertedBy, + weightsManifest: manifest + }; + if (artifacts.signature != null) { + result.signature = artifacts.signature; + } + if (artifacts.userDefinedMetadata != null) { + result.userDefinedMetadata = artifacts.userDefinedMetadata; + } + if (artifacts.modelInitializer != null) { + result.modelInitializer = artifacts.modelInitializer; + } + if (artifacts.trainingConfig != null) { + result.trainingConfig = artifacts.trainingConfig; + } + return result; +} +async function getModelArtifactsForJSON(modelJSON, loadWeights2) { + const modelArtifacts = { + modelTopology: modelJSON.modelTopology, + format: modelJSON.format, + generatedBy: modelJSON.generatedBy, + convertedBy: modelJSON.convertedBy + }; + if (modelJSON.trainingConfig != null) { + modelArtifacts.trainingConfig = modelJSON.trainingConfig; + } + if (modelJSON.weightsManifest != null) { + const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest); + modelArtifacts.weightSpecs = weightSpecs; + modelArtifacts.weightData = weightData; + } + if (modelJSON.signature != null) { + modelArtifacts.signature = modelJSON.signature; + } + if (modelJSON.userDefinedMetadata != null) { + modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata; + } + if (modelJSON.modelInitializer != null) { + modelArtifacts.modelInitializer = modelJSON.modelInitializer; + } + return modelArtifacts; +} +function getModelArtifactsInfoForJSON(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("Expected JSON model topology, received ArrayBuffer."); + } + return { + dateSaved: new Date(), + modelTopologyType: "JSON", + modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)), + weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)), + weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength + }; +} +function computeFloat16MantisaTable() { + const convertMantissa = (i) => { + let m = i << 13; + let e = 0; + while ((m & 8388608) === 0) { + e -= 8388608; + m <<= 1; + } + m &= ~8388608; + e += 947912704; + return m | e; + }; + const mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (let i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (let i = 1024; i < 2048; i++) { + mantisaTable[i] = 939524096 + (i - 1024 << 13); + } + return mantisaTable; +} +function computeFloat16ExponentTable() { + const exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 1199570944; + exponentTable[32] = 2147483648; + exponentTable[63] = 3347054592; + for (let i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (let i = 33; i < 63; i++) { + exponentTable[i] = 2147483648 + (i - 32 << 23); + } + return exponentTable; +} +function computeFloat16OffsetTable() { + const offsetTable = new Uint32Array(64); + for (let i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; +} +function getFloat16Decoder() { + const mantisaTable = computeFloat16MantisaTable(); + const exponentTable = computeFloat16ExponentTable(); + const offsetTable = computeFloat16OffsetTable(); + return (quantizedArray) => { + const buffer2 = new ArrayBuffer(4 * quantizedArray.length); + const bufferUint32View = new Uint32Array(buffer2); + for (let index = 0; index < quantizedArray.length; index++) { + const float16Bits = quantizedArray[index]; + const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10]; + bufferUint32View[index] = float32Bits; + } + return new Float32Array(buffer2); + }; +} +var IORouterRegistry = class { + constructor() { + this.saveRouters = []; + this.loadRouters = []; + } + static getInstance() { + if (IORouterRegistry.instance == null) { + IORouterRegistry.instance = new IORouterRegistry(); + } + return IORouterRegistry.instance; + } + static registerSaveRouter(saveRouter) { + IORouterRegistry.getInstance().saveRouters.push(saveRouter); + } + static registerLoadRouter(loadRouter) { + IORouterRegistry.getInstance().loadRouters.push(loadRouter); + } + static getSaveHandlers(url) { + return IORouterRegistry.getHandlers(url, "save"); + } + static getLoadHandlers(url, loadOptions) { + return IORouterRegistry.getHandlers(url, "load", loadOptions); + } + static getHandlers(url, handlerType, loadOptions) { + const validHandlers = []; + const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters; + routers.forEach((router) => { + const handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; + } +}; +var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter); +var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter); +var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url); +var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions); +var DATABASE_NAME = "tensorflowjs"; +var DATABASE_VERSION = 1; +var MODEL_STORE_NAME = "models_store"; +var INFO_STORE_NAME = "model_info_store"; +function getIndexedDBFactory() { + if (!env().getBool("IS_BROWSER")) { + throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); + } + const theWindow = typeof window === "undefined" ? self : window; + const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB; + if (factory == null) { + throw new Error("The current browser does not appear to support IndexedDB."); + } + return factory; +} +function setUpDatabase(openRequest) { + const db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" }); + db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" }); +} +var BrowserIndexedDB = class { + constructor(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } + return this.databaseAction(this.modelPath, modelArtifacts); + } + async load() { + return this.databaseAction(this.modelPath); + } + databaseAction(modelPath, modelArtifacts) { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + if (modelArtifacts == null) { + const modelTx = db.transaction(MODEL_STORE_NAME, "readonly"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const getRequest = modelStore.get(this.modelPath); + getRequest.onsuccess = () => { + if (getRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); + } else { + resolve(getRequest.result.modelArtifacts); + } + }; + getRequest.onerror = (error) => { + db.close(); + return reject(getRequest.error); + }; + modelTx.oncomplete = () => db.close(); + } else { + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + let infoStore = infoTx.objectStore(INFO_STORE_NAME); + const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo }); + let modelTx; + putInfoRequest.onsuccess = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const putModelRequest = modelStore.put({ + modelPath: this.modelPath, + modelArtifacts, + modelArtifactsInfo + }); + putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo }); + putModelRequest.onerror = (error) => { + infoStore = infoTx.objectStore(INFO_STORE_NAME); + const deleteInfoRequest = infoStore.delete(this.modelPath); + deleteInfoRequest.onsuccess = () => { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = (error2) => { + db.close(); + return reject(putModelRequest.error); + }; + }; + }; + putInfoRequest.onerror = (error) => { + db.close(); + return reject(putInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + } + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +BrowserIndexedDB.URL_SCHEME = "indexeddb://"; +var indexedDBRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) { + return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(indexedDBRouter); +IORouterRegistry.registerLoadRouter(indexedDBRouter); +function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); +} +function maybeStripScheme(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key; +} +var BrowserIndexedDBManager = class { + constructor() { + this.indexedDB = getIndexedDBFactory(); + } + async listModels() { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const tx = db.transaction(INFO_STORE_NAME, "readonly"); + const store = tx.objectStore(INFO_STORE_NAME); + const getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = () => { + const out = {}; + for (const item of getAllInfoRequest.result) { + out[item.modelPath] = item.modelArtifactsInfo; + } + resolve(out); + }; + getAllInfoRequest.onerror = (error) => { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = () => db.close(); + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } + async removeModel(path) { + path = maybeStripScheme(path); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + const infoStore = infoTx.objectStore(INFO_STORE_NAME); + const getInfoRequest = infoStore.get(path); + let modelTx; + getInfoRequest.onsuccess = () => { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`)); + } else { + const deleteInfoRequest = infoStore.delete(path); + const deleteModelData = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo); + deleteModelRequest.onerror = (error) => reject(getInfoRequest.error); + }; + deleteInfoRequest.onsuccess = deleteModelData; + deleteInfoRequest.onerror = (error) => { + deleteModelData(); + db.close(); + return reject(getInfoRequest.error); + }; + } + }; + getInfoRequest.onerror = (error) => { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +var PATH_SEPARATOR = "/"; +var PATH_PREFIX = "tensorflowjs_models"; +var INFO_SUFFIX = "info"; +var MODEL_TOPOLOGY_SUFFIX = "model_topology"; +var WEIGHT_SPECS_SUFFIX = "weight_specs"; +var WEIGHT_DATA_SUFFIX = "weight_data"; +var MODEL_METADATA_SUFFIX = "model_metadata"; +function getModelKeys(path) { + return { + info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR), + topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR), + weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR), + weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR), + modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR) + }; +} +function removeItems(keys) { + for (const key of Object.values(keys)) { + window.localStorage.removeItem(key); + } +} +function getModelPathFromKey(key) { + const items = key.split(PATH_SEPARATOR); + if (items.length < 3) { + throw new Error(`Invalid key format: ${key}`); + } + return items.slice(1, items.length - 1).join(PATH_SEPARATOR); +} +function maybeStripScheme2(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key; +} +var BrowserLocalStorage = class { + constructor(modelPath) { + if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") { + throw new Error("The current environment does not support local storage."); + } + this.LS = window.localStorage; + if (modelPath == null || !modelPath) { + throw new Error("For local storage, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + this.keys = getModelKeys(this.modelPath); + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } else { + const topology = JSON.stringify(modelArtifacts.modelTopology); + const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + try { + this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo)); + this.LS.setItem(this.keys.topology, topology); + this.LS.setItem(this.keys.weightSpecs, weightSpecs); + this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData)); + const metadata = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0, + userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0, + modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0, + trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0 + }; + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata)); + return { modelArtifactsInfo }; + } catch (err) { + removeItems(this.keys); + throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`); + } + } + } + async load() { + const info2 = JSON.parse(this.LS.getItem(this.keys.info)); + if (info2 == null) { + throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); + } + if (info2.modelTopologyType !== "JSON") { + throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); + } + const out = {}; + const topology = JSON.parse(this.LS.getItem(this.keys.topology)); + if (topology == null) { + throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); + } + out.modelTopology = topology; + const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); + if (weightSpecs == null) { + throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); + } + out.weightSpecs = weightSpecs; + const metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + const metadata = JSON.parse(metadataString); + out.format = metadata.format; + out.generatedBy = metadata.generatedBy; + out.convertedBy = metadata.convertedBy; + if (metadata.signature != null) { + out.signature = metadata.signature; + } + if (metadata.userDefinedMetadata != null) { + out.userDefinedMetadata = metadata.userDefinedMetadata; + } + if (metadata.modelInitializer != null) { + out.modelInitializer = metadata.modelInitializer; + } + if (metadata.trainingConfig != null) { + out.trainingConfig = metadata.trainingConfig; + } + } + const weightDataBase64 = this.LS.getItem(this.keys.weightData); + if (weightDataBase64 == null) { + throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); + } + out.weightData = base64StringToArrayBuffer(weightDataBase64); + return out; + } +}; +BrowserLocalStorage.URL_SCHEME = "localstorage://"; +var localStorageRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) { + return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(localStorageRouter); +IORouterRegistry.registerLoadRouter(localStorageRouter); +function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); +} +var BrowserLocalStorageManager = class { + constructor() { + assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser"); + assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage"); + this.LS = window.localStorage; + } + async listModels() { + const out = {}; + const prefix = PATH_PREFIX + PATH_SEPARATOR; + const suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (let i = 0; i < this.LS.length; ++i) { + const key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + const modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return out; + } + async removeModel(path) { + path = maybeStripScheme2(path); + const keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error(`Cannot find model at path '${path}'`); + } + const info2 = JSON.parse(this.LS.getItem(keys.info)); + removeItems(keys); + return info2; + } +}; +var URL_SCHEME_SUFFIX = "://"; +var ModelStoreManagerRegistry = class { + constructor() { + this.managers = {}; + } + static getInstance() { + if (ModelStoreManagerRegistry.instance == null) { + ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry(); + } + return ModelStoreManagerRegistry.instance; + } + static registerManager(scheme, manager) { + assert(scheme != null, () => "scheme must not be undefined or null."); + if (scheme.endsWith(URL_SCHEME_SUFFIX)) { + scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX)); + } + assert(scheme.length > 0, () => "scheme must not be an empty string."); + const registry = ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`); + registry.managers[scheme] = manager; + } + static getManager(scheme) { + const manager = this.getInstance().managers[scheme]; + if (manager == null) { + throw new Error(`Cannot find model manager for scheme '${scheme}'`); + } + return manager; + } + static getSchemes() { + return Object.keys(this.getInstance().managers); + } +}; +function parseURL(url) { + if (url.indexOf(URL_SCHEME_SUFFIX) === -1) { + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`); + } + return { + scheme: url.split(URL_SCHEME_SUFFIX)[0], + path: url.split(URL_SCHEME_SUFFIX)[1] + }; +} +async function cloneModelInternal(sourceURL, destURL, deleteSource = false) { + assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`); + const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`); + assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`); + const loadHandler = loadHandlers[0]; + const saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`); + assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`); + const saveHandler = saveHandlers[0]; + const sourceScheme = parseURL(sourceURL).scheme; + const sourcePath = parseURL(sourceURL).path; + const sameMedium = sourceScheme === parseURL(sourceURL).scheme; + const modelArtifacts = await loadHandler.load(); + if (deleteSource && sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + const saveResult = await saveHandler.save(modelArtifacts); + if (deleteSource && !sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + return saveResult.modelArtifactsInfo; +} +async function listModels() { + const schemes = ModelStoreManagerRegistry.getSchemes(); + const out = {}; + for (const scheme of schemes) { + const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels(); + for (const path in schemeOut) { + const url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; + } + } + return out; +} +async function removeModel(url) { + const schemeAndPath = parseURL(url); + const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return manager.removeModel(schemeAndPath.path); +} +async function copyModel(sourceURL, destURL) { + const deleteSource = false; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +async function moveModel(sourceURL, destURL) { + const deleteSource = true; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +var PlatformBrowser = class { + fetch(path, init2) { + return fetch(path, init2); + } + now() { + return performance.now(); + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`); + } + if (this.textEncoder == null) { + this.textEncoder = new TextEncoder(); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); + } +}; +if (env().get("IS_BROWSER")) { + env().setPlatform("browser", new PlatformBrowser()); + try { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } catch (err) { + } + try { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } catch (err) { + } +} +var getNodeFetch = { + importFetch: () => require_browser() +}; +var systemFetch; +var PlatformNode = class { + constructor() { + this.util = __require2("util"); + this.textEncoder = new this.util.TextEncoder(); + } + fetch(path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); + } + now() { + const time2 = process.hrtime(); + return time2[0] * 1e3 + time2[1] / 1e6; + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + if (bytes.length === 0) { + return ""; + } + return new this.util.TextDecoder(encoding).decode(bytes); + } +}; +if (env().get("IS_NODE")) { + env().setPlatform("node", new PlatformNode()); +} +function buffer(shape, dtype = "float32", values) { + dtype = dtype || "float32"; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); +} +function cast_(x, dtype) { + const $x = convertToTensor(x, "x", "cast"); + if (!isValidDtype(dtype)) { + throw new Error(`Failed to cast to unknown dtype ${dtype}`); + } + if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") { + throw new Error("Only strings can be casted to strings"); + } + const inputs = { x: $x }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); +} +var cast = op({ cast_ }); +function clone_(x) { + const $x = convertToTensor(x, "x", "clone", "string_or_numeric"); + const inputs = { x: $x }; + return ENGINE.runKernel(Identity, inputs); +} +var clone = op({ clone_ }); +function print2(x, verbose = false) { + console.log(x.toString(verbose)); +} +getOrMakeEngine(); +var opHandler2 = { + buffer, + cast, + clone, + print: print2 +}; +setOpHandler(opHandler2); +var io_exports = {}; +__export2(io_exports, { + browserFiles: () => browserFiles, + browserHTTPRequest: () => browserHTTPRequest, + concatenateArrayBuffers: () => concatenateArrayBuffers, + copyModel: () => copyModel, + decodeWeights: () => decodeWeights, + encodeWeights: () => encodeWeights, + fromMemory: () => fromMemory, + getLoadHandlers: () => getLoadHandlers, + getModelArtifactsForJSON: () => getModelArtifactsForJSON, + getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON, + getSaveHandlers: () => getSaveHandlers, + http: () => http, + isHTTPScheme: () => isHTTPScheme, + listModels: () => listModels, + loadWeights: () => loadWeights, + moveModel: () => moveModel, + registerLoadRouter: () => registerLoadRouter, + registerSaveRouter: () => registerSaveRouter, + removeModel: () => removeModel, + weightsLoaderFactory: () => weightsLoaderFactory, + withSaveHandler: () => withSaveHandler +}); +var DEFAULT_FILE_NAME_PREFIX = "model"; +var DEFAULT_JSON_EXTENSION_NAME = ".json"; +var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; +function defer(f) { + return new Promise((resolve) => setTimeout(resolve)).then(f); +} +var BrowserDownloads = class { + constructor(fileNamePrefix) { + if (!env().getBool("IS_BROWSER")) { + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + } + if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length); + } + if (fileNamePrefix == null || fileNamePrefix.length === 0) { + fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; + } + this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME; + this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME; + } + async save(modelArtifacts) { + if (typeof document === "undefined") { + throw new Error("Browser downloads are not supported in this environment since `document` is not present"); + } + const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: "application/octet-stream" })); + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); + } else { + const weightsManifest = [{ + paths: ["./" + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" })); + const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + jsonAnchor.download = this.modelJsonFileName; + jsonAnchor.href = modelJsonURL; + await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click"))); + if (modelArtifacts.weightData != null) { + const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + weightDataAnchor.download = this.weightDataFileName; + weightDataAnchor.href = weightsURL; + await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click"))); + } + return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }; + } + } +}; +BrowserDownloads.URL_SCHEME = "downloads://"; +var BrowserFiles = class { + constructor(files) { + if (files == null || files.length < 1) { + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`); + } + this.jsonFile = files[0]; + this.weightsFiles = files.slice(1); + } + async load() { + return new Promise((resolve, reject) => { + const jsonReader = new FileReader(); + jsonReader.onload = (event) => { + const modelJSON = JSON.parse(event.target.result); + const modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + return; + } + const weightsManifest = modelJSON.weightsManifest; + if (weightsManifest == null) { + reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + return; + } + if (this.weightsFiles.length === 0) { + resolve({ modelTopology }); + return; + } + const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); + resolve(modelArtifactsPromise); + }; + jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`); + jsonReader.readAsText(this.jsonFile); + }); + } + loadWeights(weightsManifest) { + const weightSpecs = []; + const paths = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + paths.push(...entry.paths); + } + const pathToFile = this.checkManifestAndWeightFiles(weightsManifest); + const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path])); + return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]); + } + loadWeightsFile(path, file) { + return new Promise((resolve, reject) => { + const weightFileReader = new FileReader(); + weightFileReader.onload = (event) => { + const weightData = event.target.result; + resolve(weightData); + }; + weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`); + weightFileReader.readAsArrayBuffer(file); + }); + } + checkManifestAndWeightFiles(manifest) { + const basenames = []; + const fileNames = this.weightsFiles.map((file) => basename(file.name)); + const pathToFile = {}; + for (const group of manifest) { + group.paths.forEach((path) => { + const pathBasename = basename(path); + if (basenames.indexOf(pathBasename) !== -1) { + throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`); + } + basenames.push(pathBasename); + if (fileNames.indexOf(pathBasename) === -1) { + throw new Error(`Weight file with basename '${pathBasename}' is not provided.`); + } else { + pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)]; + } + }); + } + if (basenames.length !== this.weightsFiles.length) { + throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + } + return pathToFile; + } +}; +var browserDownloadsRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) { + return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(browserDownloadsRouter); +function browserDownloads(fileNamePrefix = "model") { + return new BrowserDownloads(fileNamePrefix); +} +function browserFiles(files) { + return new BrowserFiles(files); +} +function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + let resolvedPromise = 0; + const registerMonitor = (promise) => { + promise.then((value) => { + const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction); + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises2) { + assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array"); + } + function checkFraction(startFraction2, endFraction2) { + assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`); + assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`); + assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`); + } + return Promise.all(promises.map(registerMonitor)); +} +async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + if (loadOptions == null) { + loadOptions = {}; + } + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true })); + const fetchStartFraction = 0; + const fetchEndFraction = 0.5; + const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction); + const bufferPromises = responses.map((response) => response.arrayBuffer()); + const bufferStartFraction = 0.5; + const bufferEndFraction = 1; + const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction); + return buffers; +} +async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) { + const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit }); + const loadWeights2 = weightsLoaderFactory(fetchWeights); + return loadWeights2(manifest, filePathPrefix, weightNames); +} +function weightsLoaderFactory(fetchWeightsFunction) { + return async (manifest, filePathPrefix = "", weightNames) => { + const groupIndicesToFetchMap = manifest.map(() => false); + const groupWeightsToFetch = {}; + const weightsFound = weightNames != null ? weightNames.map(() => false) : []; + const allManifestWeightNames = []; + manifest.forEach((manifestGroupConfig, groupIndex) => { + let groupOffset = 0; + manifestGroupConfig.weights.forEach((weightsEntry) => { + const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype; + const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape); + const enqueueWeightsForFetchingFn = () => { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach((weightName, weightIndex) => { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; + }); + }); + if (!weightsFound.every((found) => found)) { + const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]); + throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`); + } + const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => { + if (shouldFetch) { + accumulator.push(i); + } + return accumulator; + }, []); + const fetchUrls = []; + groupIndicesToFetch.forEach((i) => { + manifest[i].paths.forEach((filepath) => { + const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath; + fetchUrls.push(fetchUrl); + }); + }); + const buffers = await fetchWeightsFunction(fetchUrls); + const weightsTensorMap = {}; + let bufferIndexOffset = 0; + groupIndicesToFetch.forEach((i) => { + const numBuffers = manifest[i].paths.length; + let groupBytes = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + groupBytes += buffers[bufferIndexOffset + i2].byteLength; + } + const groupBuffer = new ArrayBuffer(groupBytes); + const groupByteBuffer = new Uint8Array(groupBuffer); + let groupBufferOffset = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]); + groupByteBuffer.set(buffer2, groupBufferOffset); + groupBufferOffset += buffer2.byteLength; + } + const weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach((weightsEntry) => { + const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (const name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return weightsTensorMap; + }; +} +var OCTET_STREAM_MIME_TYPE = "application/octet-stream"; +var JSON_TYPE = "application/json"; +var HTTPRequest = class { + constructor(path, loadOptions) { + this.DEFAULT_METHOD = "POST"; + if (loadOptions == null) { + loadOptions = {}; + } + this.weightPathPrefix = loadOptions.weightPathPrefix; + this.onProgress = loadOptions.onProgress; + this.weightUrlConverter = loadOptions.weightUrlConverter; + if (loadOptions.fetchFunc != null) { + assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"); + this.fetch = loadOptions.fetchFunc; + } else { + this.fetch = env().platform.fetch; + } + assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty."); + if (Array.isArray(path)) { + assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`); + } + this.path = path; + if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) { + throw new Error("requestInit is expected to have no pre-existing body, but has one."); + } + this.requestInit = loadOptions.requestInit || {}; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); + } + const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + init2.body = new FormData(); + const weightsManifest = [{ + paths: ["./model.weights.bin"], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json"); + if (modelArtifacts.weightData != null) { + init2.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin"); + } + const response = await this.fetch(this.path, init2); + if (response.ok) { + return { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response] + }; + } else { + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`); + } + } + async load() { + const modelConfigRequest = await this.fetch(this.path, this.requestInit); + if (!modelConfigRequest.ok) { + throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`); + } + let modelJSON; + try { + modelJSON = await modelConfigRequest.json(); + } catch (e) { + let message = `Failed to parse model JSON of response from ${this.path}.`; + if (this.path.endsWith(".pb")) { + message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository."; + } else { + message += " Please make sure the server is serving valid JSON for this request."; + } + throw new Error(message); + } + const modelTopology = modelJSON.modelTopology; + const weightsManifest = modelJSON.weightsManifest; + if (modelTopology == null && weightsManifest == null) { + throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); + } + return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); + } + async loadWeights(weightsManifest) { + const weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + const [prefix, suffix] = parseUrl(weightPath); + const pathPrefix = this.weightPathPrefix || prefix; + const weightSpecs = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + } + const fetchURLs = []; + const urlPromises = []; + for (const weightsGroup of weightsManifest) { + for (const path of weightsGroup.paths) { + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + if (this.weightUrlConverter) { + fetchURLs.push(...await Promise.all(urlPromises)); + } + const buffers = await loadWeightsAsArrayBuffer(fetchURLs, { + requestInit: this.requestInit, + fetchFunc: this.fetch, + onProgress: this.onProgress + }); + return [weightSpecs, concatenateArrayBuffers(buffers)]; + } +}; +HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; +function parseUrl(url) { + const lastSlash = url.lastIndexOf("/"); + const lastSearchParam = url.lastIndexOf("?"); + const prefix = url.substring(0, lastSlash); + const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ""; + return [prefix + "/", suffix]; +} +function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; +} +var httpRouter = (url, loadOptions) => { + if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) { + return null; + } else { + let isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every((urlItem) => isHTTPScheme(urlItem)); + } else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } + } + return null; +}; +IORouterRegistry.registerSaveRouter(httpRouter); +IORouterRegistry.registerLoadRouter(httpRouter); +function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); +} +function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); +} +var PassthroughLoader = class { + constructor(modelArtifacts) { + this.modelArtifacts = modelArtifacts; + } + async load() { + return this.modelArtifacts; + } +}; +var PassthroughSaver = class { + constructor(saveHandler) { + this.saveHandler = saveHandler; + } + async save(modelArtifacts) { + return this.saveHandler(modelArtifacts); + } +}; +function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ modelTopology: modelArtifacts }); + } + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ + modelTopology: modelArtifacts, + weightSpecs, + weightData, + trainingConfig + }); + } +} +function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); +} +var math_exports = {}; +__export2(math_exports, { + confusionMatrix: () => confusionMatrix +}); +function matMul_(a, b, transposeA = false, transposeB = false) { + let $a = convertToTensor(a, "a", "matMul"); + let $b = convertToTensor(b, "b", "matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + const attrs = { transposeA, transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); +} +var matMul = op({ matMul_ }); +function oneHot_(indices, depth, onValue = 1, offValue = 0) { + if (depth < 2) { + throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); + } + const $indices = convertToTensor(indices, "indices", "oneHot", "int32"); + const inputs = { indices: $indices }; + const attrs = { depth, onValue, offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); +} +var oneHot = op({ oneHot_ }); +function transpose_(x, perm) { + const $x = convertToTensor(x, "x", "transpose"); + if (perm == null) { + perm = $x.shape.map((s, i) => i).reverse(); + } + assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`); + perm.forEach((axis) => { + assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + const inputs = { x: $x }; + const attrs = { perm }; + return ENGINE.runKernel(Transpose, inputs, attrs); +} +var transpose = op({ transpose_ }); +function confusionMatrix_(labels2, predictions, numClasses) { + const $labels = convertToTensor(labels2, "labels", "confusionMatrix"); + const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix"); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`); + assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`); + assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`); + assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`); + assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`); + const oneHotLabels = oneHot(cast($labels, "int32"), numClasses); + const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses); + const oneHotLabelsT = transpose(oneHotLabels); + const product = matMul(oneHotLabelsT, oneHotPredictions); + return cast(product, "int32"); +} +var confusionMatrix = op({ confusionMatrix_ }); +var browser_exports = {}; +__export2(browser_exports, { + fromPixels: () => fromPixels, + fromPixelsAsync: () => fromPixelsAsync, + toPixels: () => toPixels +}); +function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error("tensor3d() requires shape to have three numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 3 && inferredShape.length !== 1) { + throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +var fromPixels2DContext; +function fromPixels_(pixels, numChannels = 3) { + if (numChannels > 4) { + throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); + } + if (pixels == null) { + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + } + let isPixelData2 = false; + let isImageData = false; + let isVideo = false; + let isImage = false; + let isCanvasLike = false; + let isImageBitmap = false; + if (pixels.data instanceof Uint8Array) { + isPixelData2 = true; + } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) { + isImageData = true; + } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) { + isVideo = true; + } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) { + isImage = true; + } else if (pixels.getContext != null) { + isCanvasLike = true; + } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) { + isImageBitmap = true; + } else { + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`); + } + if (isVideo) { + const HAVE_CURRENT_DATA_READY_STATE = 2; + if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) { + throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the