diff --git a/dist/demo-browser-index.js b/dist/demo-browser-index.js index fb752604..31126822 100644 --- a/dist/demo-browser-index.js +++ b/dist/demo-browser-index.js @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`,version14="0.9.21",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig2={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig2),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig2){this.state="load";let timeStamp=now2();userConfig2&&(this.config=mergeDeep(this.config,userConfig2)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig2={}){this.state="image",this.config=mergeDeep(this.config,userConfig2);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig2={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig2),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig2){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig2&&(this.config=mergeDeep(this.config,userConfig2));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};async function drawGesture(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.font=ui2.baseFont,ctx.fillStyle=ui2.baseLabel;let i=1;for(let gesture2=0;gesture21&&what[1].length>0){let person=where2[1]>0?`#${where2[1]}`:"",label=`${where2[0]} ${person}: ${what[1]}`;ctx.fillStyle="black",ctx.fillText(label,8,2+i*ui2.baseLineHeight),ctx.fillStyle=ui2.baseLabel,ctx.fillText(label,6,0+i*ui2.baseLineHeight),i+=1}}}}async function drawFace(result,canvas,ui2,triangulation){if(!!result){let ctx=canvas.getContext("2d");for(let face2 of result){ctx.font=ui2.baseFont,ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ui2.drawBoxes&&ctx.rect(face2.box[0],face2.box[1],face2.box[2],face2.box[3]);let labels=[];if(face2.genderConfidence&&labels.push(`${Math.trunc(100*face2.genderConfidence)}% ${face2.gender||""}`),face2.age&&labels.push(`age: ${face2.age||""}`),face2.iris&&labels.push(`iris: ${face2.iris}`),face2.emotion&&face2.emotion.length>0){let emotion2=face2.emotion.map(a=>`${Math.trunc(100*a.score)}% ${a.emotion}`);labels.push(emotion2.join(" "))}ctx.fillStyle=ui2.baseLabel;for(let i=0;iface2.mesh[index]),path=new Path2D;path.moveTo(points[0][0],points[0][1]);for(let point of points)path.lineTo(point[0],point[1]);path.closePath(),ctx.strokeStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.stroke(path),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.fill(path))}if(face2.annotations&&face2.annotations.leftEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.leftEyeIris[3][0]-face2.annotations.leftEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.leftEyeIris[4][1]-face2.annotations.leftEyeIris[2][1])/2;ctx.ellipse(face2.annotations.leftEyeIris[0][0],face2.annotations.leftEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}if(face2.annotations&&face2.annotations.rightEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.rightEyeIris[3][0]-face2.annotations.rightEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.rightEyeIris[4][1]-face2.annotations.rightEyeIris[2][1])/2;ctx.ellipse(face2.annotations.rightEyeIris[0][0],face2.annotations.rightEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}}}}}}var lastDrawnPose=[];async function drawBody(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let i=0;ia.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightShoulder"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftShoulder"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftWrist"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightWrist"),part&&path.lineTo(part.position.x,part.position.y)),ctx.stroke(path)}}}}async function drawHand(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let hand of result){if(ctx.font=ui2.baseFont,ctx.lineWidth=ui2.baseLineWidth,ui2.drawBoxes&&(ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.rect(hand.box[0],hand.box[1],hand.box[2],hand.box[3]),ctx.fillStyle="black",ctx.fillText("hand",hand.box[0]+3,1+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.fillStyle=ui2.baseLabel,ctx.fillText("hand",hand.box[0]+2,0+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.stroke()),ui2.drawPoints&&(hand.landmarks&&hand.landmarks.length>0))for(let point of hand.landmarks)ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*point[2]}, ${127.5-2*point[2]}, 255, 0.5)`:ui2.baseColor,ctx.beginPath(),ctx.arc(point[0],point[1],2,0,2*Math.PI),ctx.fill();if(ui2.drawPolygons){let addPart=part=>{if(!!part)for(let i=0;i0?i-1:0][0],part[i>0?i-1:0][1]),ctx.lineTo(part[i][0],part[i][1]),ctx.stroke()};addPart(hand.annotations.indexFinger),addPart(hand.annotations.middleFinger),addPart(hand.annotations.ringFinger),addPart(hand.annotations.pinky),addPart(hand.annotations.thumb)}}}}var draw_default={face:drawFace,body:drawBody,hand:drawHand,gesture:drawGesture},instance=0,CSScreated=!1,theme={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function createCSS(){if(!CSScreated){let css=` +2Q==`,version14="0.9.22",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig2={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig2),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig2){this.state="load";let timeStamp=now2();userConfig2&&(this.config=mergeDeep(this.config,userConfig2)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig2={}){this.state="image",this.config=mergeDeep(this.config,userConfig2);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig2={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig2),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig2){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig2&&(this.config=mergeDeep(this.config,userConfig2));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};async function drawGesture(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.font=ui2.baseFont,ctx.fillStyle=ui2.baseLabel;let i=1;for(let gesture2=0;gesture21&&what[1].length>0){let person=where2[1]>0?`#${where2[1]}`:"",label=`${where2[0]} ${person}: ${what[1]}`;ctx.fillStyle="black",ctx.fillText(label,8,2+i*ui2.baseLineHeight),ctx.fillStyle=ui2.baseLabel,ctx.fillText(label,6,0+i*ui2.baseLineHeight),i+=1}}}}async function drawFace(result,canvas,ui2,triangulation){if(!!result){let ctx=canvas.getContext("2d");for(let face2 of result){ctx.font=ui2.baseFont,ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ui2.drawBoxes&&ctx.rect(face2.box[0],face2.box[1],face2.box[2],face2.box[3]);let labels=[];if(face2.genderConfidence&&labels.push(`${Math.trunc(100*face2.genderConfidence)}% ${face2.gender||""}`),face2.age&&labels.push(`age: ${face2.age||""}`),face2.iris&&labels.push(`iris: ${face2.iris}`),face2.emotion&&face2.emotion.length>0){let emotion2=face2.emotion.map(a=>`${Math.trunc(100*a.score)}% ${a.emotion}`);labels.push(emotion2.join(" "))}ctx.fillStyle=ui2.baseLabel;for(let i=0;iface2.mesh[index]),path=new Path2D;path.moveTo(points[0][0],points[0][1]);for(let point of points)path.lineTo(point[0],point[1]);path.closePath(),ctx.strokeStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.stroke(path),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.fill(path))}if(face2.annotations&&face2.annotations.leftEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.leftEyeIris[3][0]-face2.annotations.leftEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.leftEyeIris[4][1]-face2.annotations.leftEyeIris[2][1])/2;ctx.ellipse(face2.annotations.leftEyeIris[0][0],face2.annotations.leftEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}if(face2.annotations&&face2.annotations.rightEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.rightEyeIris[3][0]-face2.annotations.rightEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.rightEyeIris[4][1]-face2.annotations.rightEyeIris[2][1])/2;ctx.ellipse(face2.annotations.rightEyeIris[0][0],face2.annotations.rightEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}}}}}}var lastDrawnPose=[];async function drawBody(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let i=0;ia.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightShoulder"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftShoulder"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftWrist"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightWrist"),part&&path.lineTo(part.position.x,part.position.y)),ctx.stroke(path)}}}}async function drawHand(result,canvas,ui2){if(!!result){let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let hand of result){if(ctx.font=ui2.baseFont,ctx.lineWidth=ui2.baseLineWidth,ui2.drawBoxes&&(ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.rect(hand.box[0],hand.box[1],hand.box[2],hand.box[3]),ctx.fillStyle="black",ctx.fillText("hand",hand.box[0]+3,1+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.fillStyle=ui2.baseLabel,ctx.fillText("hand",hand.box[0]+2,0+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.stroke()),ui2.drawPoints&&(hand.landmarks&&hand.landmarks.length>0))for(let point of hand.landmarks)ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*point[2]}, ${127.5-2*point[2]}, 255, 0.5)`:ui2.baseColor,ctx.beginPath(),ctx.arc(point[0],point[1],2,0,2*Math.PI),ctx.fill();if(ui2.drawPolygons){let addPart=part=>{if(!!part)for(let i=0;i0?i-1:0][0],part[i>0?i-1:0][1]),ctx.lineTo(part[i][0],part[i][1]),ctx.stroke()};addPart(hand.annotations.indexFinger),addPart(hand.annotations.middleFinger),addPart(hand.annotations.ringFinger),addPart(hand.annotations.pinky),addPart(hand.annotations.thumb)}}}}var draw_default={face:drawFace,body:drawBody,hand:drawHand,gesture:drawGesture},instance=0,CSScreated=!1,theme={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function createCSS(){if(!CSScreated){let css=` :root { --rounded: 0.2rem; } .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10; box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; } diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index 9becc4d1..13bfcf28 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -5,7 +5,7 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import{version as tfjs}from"@tensorflow/tfjs/package.json";import{version as versionCore}from"@tensorflow/tfjs-core/package.json";import{version as versionData}from"@tensorflow/tfjs-data/package.json";import{version as versionLayers}from"@tensorflow/tfjs-layers/package.json";import{version as versionConverter}from"@tensorflow/tfjs-converter/package.json";import{version_cpu}from"@tensorflow/tfjs-backend-cpu/dist/index.js";import{version_webgl}from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{version_wasm}from"@tensorflow/tfjs-backend-wasm/dist/index.js";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 require_tfjs_esm=__commonJS(exports=>{__export(exports,{data:()=>data,version:()=>version2});__exportStar(exports,dist_star);__exportStar(exports,dist_star2);__exportStar(exports,dist_star3);__exportStar(exports,dist_star4);__exportStar(exports,dist_star5);__exportStar(exports,dist_star6);var version2={tfjs,"tfjs-core":versionCore,"tfjs-data":versionData,"tfjs-layers":versionLayers,"tfjs-converter":versionConverter,"tfjs-backend-cpu":version_cpu,"tfjs-backend-webgl":version_webgl,"tfjs-backend-wasm":version_wasm}}),require_blazeface=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports.load=load2;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox}),require_box=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2}),require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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28,17,22,29,25,28,30,26,27,31,29],TRI7=[0,4,1,2,4,3,4,5,6],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],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],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline}),require_facemesh=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load2;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords.TRI468}),require_profile=__commonJS(exports=>{var profileData={};function profile2(name,data3){if(!(!data3||!data3.kernels)){let maxResults=5,time=data3.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data3.newBytes,newTensors:data3.newTensors,peakBytes:data3.peakBytes,numKernelOps:data3.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports.run=profile2}),require_age=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data3=ageT.dataSync();obj.age=Math.trunc(10*data3[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_gender=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data3=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data3[0]-data3[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]>data3[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data3[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_emotion=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data3;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data3=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data3=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data3[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_embedding=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data3=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data3=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data3=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data3)}):null}exports.predict=predict2;exports.simmilarity=simmilarity2;exports.load=load2}),require_modelBase=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel}),require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.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"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports.clampVector=clampVector}),require_decoders=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d}),require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load2}),require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose}),require_handdetector=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports=>{exports.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_cent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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load2}),require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import{version as tfjs}from"@tensorflow/tfjs/package.json";import{version as versionCore}from"@tensorflow/tfjs-core/package.json";import{version as versionData}from"@tensorflow/tfjs-data/package.json";import{version as versionLayers}from"@tensorflow/tfjs-layers/package.json";import{version as versionConverter}from"@tensorflow/tfjs-converter/package.json";import{version_cpu}from"@tensorflow/tfjs-backend-cpu/dist/index.js";import{version_webgl}from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{version_wasm}from"@tensorflow/tfjs-backend-wasm/dist/index.js";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 require_tfjs_esm=__commonJS(exports=>{__export(exports,{data:()=>data,version:()=>version2});__exportStar(exports,dist_star);__exportStar(exports,dist_star2);__exportStar(exports,dist_star3);__exportStar(exports,dist_star4);__exportStar(exports,dist_star5);__exportStar(exports,dist_star6);var version2={tfjs,"tfjs-core":versionCore,"tfjs-data":versionData,"tfjs-layers":versionLayers,"tfjs-converter":versionConverter,"tfjs-backend-cpu":version_cpu,"tfjs-backend-webgl":version_webgl,"tfjs-backend-wasm":version_wasm}}),require_blazeface=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports.load=load2;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox}),require_box=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2}),require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,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],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],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline}),require_facemesh=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load2;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords.TRI468}),require_profile=__commonJS(exports=>{var profileData={};function profile2(name,data3){if(!(!data3||!data3.kernels)){let maxResults=5,time=data3.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data3.newBytes,newTensors:data3.newTensors,peakBytes:data3.peakBytes,numKernelOps:data3.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports.run=profile2}),require_age=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data3=ageT.dataSync();obj.age=Math.trunc(10*data3[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_gender=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data3=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data3[0]-data3[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]>data3[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data3[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_emotion=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data3;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data3=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data3=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data3[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_embedding=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data3=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data3=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data3=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data3)}):null}exports.predict=predict2;exports.simmilarity=simmilarity2;exports.load=load2}),require_modelBase=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel}),require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.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"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports.clampVector=clampVector}),require_decoders=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d}),require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load2}),require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose}),require_handdetector=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports=>{exports.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_cent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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load2}),require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let 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,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(` `),_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(` @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.21",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose();let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{Human as default}; +2Q==`;var version="0.9.22",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{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 befd13ac..d34c5819 100644 --- a/dist/human.esm-nobundle.js.map +++ b/dist/human.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/tfjs/tf-browser.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/log.js", "../src/human.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\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) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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, 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[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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n 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1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\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) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.esm.js b/dist/human.esm.js index 4fb0f6ac..06823c2d 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version14="0.9.21",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{Human as default}; +2Q==`;var version14="0.9.22",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{Human as default}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.js b/dist/human.js index 0a054191..547f5c76 100644 --- a/dist/human.js +++ b/dist/human.js @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version14="0.9.21",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};return human_exports;})(); +2Q==`;var version14="0.9.22",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};return human_exports;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js index 7b165888..61d47189 100644 --- a/dist/human.node-gpu.js +++ b/dist/human.node-gpu.js @@ -5,7 +5,7 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))}),require_blazeface=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox}),require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2}),require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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28,17,22,29,25,28,30,26,27,31,29],TRI7=[0,4,1,2,4,3,4,5,6],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],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],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline}),require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468}),require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!(!data2||!data2.kernels)){let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports2.run=profile2}),require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2}),require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel}),require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.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"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector}),require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d}),require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2}),require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose}),require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports2=>{exports2.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_c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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2}),require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))}),require_blazeface=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox}),require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2}),require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,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],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],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline}),require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468}),require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!(!data2||!data2.kernels)){let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports2.run=profile2}),require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2}),require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel}),require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.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"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector}),require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d}),require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2}),require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose}),require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports2=>{exports2.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_c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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2}),require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let 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,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(` `),_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(` @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.21",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose();let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; +2Q==`;var version="0.9.22",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; //# sourceMappingURL=human.node-gpu.js.map diff --git a/dist/human.node-gpu.js.map b/dist/human.node-gpu.js.map index 70b9183b..569457d5 100644 --- a/dist/human.node-gpu.js.map +++ b/dist/human.node-gpu.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/tfjs/tf-node-gpu.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 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1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.node.js b/dist/human.node.js index 184da9f8..b81dd16b 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -5,7 +5,7 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of 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starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox}),require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2}),require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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28,17,22,29,25,28,30,26,27,31,29],TRI7=[0,4,1,2,4,3,4,5,6],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],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],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline}),require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468}),require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!(!data2||!data2.kernels)){let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports2.run=profile2}),require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2}),require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel}),require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.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"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector}),require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d}),require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2}),require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose}),require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports2=>{exports2.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_c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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2}),require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))}),require_blazeface=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox}),require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2}),require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,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],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],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline}),require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[])if(!prediction.isDisposedInternal){let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468}),require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!(!data2||!data2.kernels)){let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}}exports2.run=profile2}),require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2}),require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2}),require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel}),require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}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){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,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"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.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"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector}),require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d}),require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.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:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2}),require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose}),require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports2=>{exports2.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_c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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2}),require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let 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]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.hand=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!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(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_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,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let 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,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return 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),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_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)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},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(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let 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,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(` `),_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(` @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.21",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose();let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; +2Q==`;var version="0.9.22",now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!!this.analyzeMemoryLeaks){let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; //# sourceMappingURL=human.node.js.map diff --git a/dist/human.node.js.map b/dist/human.node.js.map index fe5e085c..31d49be1 100644 --- a/dist/human.node.js.map +++ b/dist/human.node.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/tfjs/tf-node.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 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1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.5625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\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\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction 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}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction 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}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction 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}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.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 */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\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 */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction 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}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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}\nfunction 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}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction 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}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "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\nconst 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\nconst 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\nconst 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\nconst 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];\nconst 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];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst 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];\nconst 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];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\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 const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || 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.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (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 // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n 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 const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.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\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction 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}\nexports.getImageCoords = getImageCoords;\n\nfunction 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}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction 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}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\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\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (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\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (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 _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\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\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\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\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\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\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\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\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\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\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\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\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\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\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\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\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\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let 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\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\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\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\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\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\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\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\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\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\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\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\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\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\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\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\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\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\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\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\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\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.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 this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (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 log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nfunction 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}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction 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}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\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 // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction 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}\nfunction 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}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.node.json b/dist/human.node.json index f62380b6..53ef727f 100644 --- a/dist/human.node.json +++ b/dist/human.node.json @@ -40,7 +40,7 @@ "imports": [] }, "src/face/coords.js": { - "bytes": 37909, + "bytes": 37915, "imports": [] }, "src/face/facepipeline.js": { @@ -362,7 +362,7 @@ "imports": [] }, "src/human.js": { - "bytes": 17375, + "bytes": 17518, "imports": [ { "path": "src/log.js" @@ -419,7 +419,7 @@ "dist/human.node-gpu.js.map": { "imports": [], "inputs": {}, - "bytes": 700216 + "bytes": 700318 }, "dist/human.node-gpu.js": { "imports": [], @@ -438,7 +438,7 @@ "bytesInOutput": 2424 }, "src/face/coords.js": { - "bytesInOutput": 30828 + "bytesInOutput": 30724 }, "src/face/facepipeline.js": { "bytesInOutput": 9431 @@ -516,7 +516,7 @@ "bytesInOutput": 3665 }, "src/human.js": { - "bytesInOutput": 11238 + "bytesInOutput": 11346 }, "src/log.js": { "bytesInOutput": 266 @@ -540,7 +540,7 @@ "bytesInOutput": 20 } }, - "bytes": 308544 + "bytes": 308548 } } } diff --git a/package-lock.json b/package-lock.json index 45d21e16..f85a3623 100644 --- a/package-lock.json +++ b/package-lock.json @@ -356,7 +356,8 @@ "@types/node": { "version": "14.14.20", "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.20.tgz", - "integrity": "sha512-Y93R97Ouif9JEOWPIUyU+eyIdyRqQR0I8Ez1dzku4hDx34NWh4HbtIc3WNzwB1Y9ULvNGeu5B8h8bVL5cAk4/A==" + "integrity": "sha512-Y93R97Ouif9JEOWPIUyU+eyIdyRqQR0I8Ez1dzku4hDx34NWh4HbtIc3WNzwB1Y9ULvNGeu5B8h8bVL5cAk4/A==", + "dev": true }, "@types/node-fetch": { "version": "2.5.7", @@ -452,12 +453,14 @@ "ansi-regex": { "version": "5.0.0", "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.0.tgz", - "integrity": "sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg==" + "integrity": "sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg==", + "dev": true }, "ansi-styles": { "version": "4.3.0", "resolved": 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"https://registry.npmjs.org/table/-/table-6.0.7.tgz", "integrity": "sha512-rxZevLGTUzWna/qBLObOe16kB2RTnnbhciwgPbMMlazz1yZGVEgnZK762xyVdVznhqxrfCeBMmMkgOOaPwjH7g==", + "dev": true, "requires": { "ajv": "^7.0.2", "lodash": "^4.17.20", @@ -2447,6 +2463,7 @@ "version": "7.0.3", "resolved": "https://registry.npmjs.org/ajv/-/ajv-7.0.3.tgz", "integrity": "sha512-R50QRlXSxqXcQP5SvKUrw8VZeypvo12i2IX0EeR5PiZ7bEKeHWgzgo264LDadUsCU42lTJVhFikTqJwNeH34gQ==", + "dev": true, "requires": { "fast-deep-equal": "^3.1.1", "json-schema-traverse": "^1.0.0", @@ -2457,7 +2474,8 @@ "json-schema-traverse": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz", - "integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==" + "integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==", + "dev": true } } }, @@ -2522,6 +2540,7 @@ "version": "4.4.0", "resolved": "https://registry.npmjs.org/uri-js/-/uri-js-4.4.0.tgz", "integrity": "sha512-B0yRTzYdUCCn9n+F4+Gh4yIDtMQcaJsmYBDsTSG8g/OejKBodLQ2IHfN3bM7jUsRXndopT7OIXWdYqc1fjmV6g==", + "dev": true, "requires": { "punycode": "^2.1.0" } diff --git a/wiki b/wiki index ad583a5a..766a4e25 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit ad583a5aa931611f981f1d2287e98343df5393d8 +Subproject commit 766a4e2504b1667dcf79bd4f184913912f0f4ea2