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face-api/dist/face-api.node.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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Ra={};Lr(Ra,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>so,FaceDetection:()=>M,FaceDetectionNet:()=>lo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>po,FaceLandmarks:()=>z,FaceLandmarks5:()=>jr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Tr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>g,PredictedBox:()=>Ur,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Aa,allFacesSsdMobilenetv1:()=>fn,allFacesTinyYolov2:()=>La,awaitMediaLoaded:()=>to,bufferToImage:()=>eo,computeFaceDescriptor:()=>va,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>fa,createFaceRecognitionNet:()=>ea,createSsdMobilenetv1:()=>qo,createTinyFaceDetector:()=>Wa,createTinyYolov2:()=>ha,detectAllFaces:()=>Sr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>xa,detectLandmarks:()=>Na,detectSingleFace:()=>Sa,draw:()=>co,env:()=>w,euclideanDistance:()=>vo,extendWithAge:()=>Er,extendWithFaceDescriptor:()=>Dr,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>xr,extendWithFaceLandmarks:()=>Pe,extendWithGender:()=>Mr,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>Hn,fetchJson:()=>no,fetchNetWeights:()=>zn,fetchOrThrow:()=>xt,fetchVideo:()=>Vn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>ro,imageToSquare:()=>oo,inverseSigmoid:()=>An,iou:()=>zr,isMediaElement:()=>ir,isMediaLoaded:()=>Ae,isWithAge:()=>ra,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>io,isWithFaceLandmarks:()=>Zt,isWithGender:()=>oa,loadAgeGenderModel:()=>Ma,loadFaceDetectionModel:()=>Ca,loadFaceExpressionModel:()=>Ea,loadFaceLandmarkModel:()=>Pa,loadFaceLandmarkTinyModel:()=>Fa,loadFaceRecognitionModel:()=>Da,loadSsdMobilenetv1Model:()=>un,loadTinyFaceDetectorModel:()=>Ta,loadTinyYolov2Model:()=>wa,loadWeightMap:()=>ao,locateFaces:()=>Ia,matchDimensions:()=>Yn,minBbox:()=>Vr,nets:()=>P,nonMaxSuppression:()=>Yr,normalize:()=>rt,padToSquare:()=>Gr,predictAgeAndGender:()=>_a,recognizeFaceExpressions:()=>ya,resizeResults:()=>ln,resolveInput:()=>Ut,shuffleArray:()=>Ln,sigmoid:()=>Ne,ssdMobilenetv1:()=>mn,tf:()=>ka,tinyFaceDetector:()=>ba,tinyYolov2:()=>ga,toNetInput:()=>C,utils:()=>Hr,validateConfig:()=>ho,version:()=>Ba});module.exports=yn(Ra);var 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this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(zt)}};var pe=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Ht(this.distance)})`:""}`}};var ue=class extends D{constructor(e,r){super(e);this._label=r}static assertIsValidLabeledBox(e,r){if(D.assertIsValidBox(e,r),!et(e.label))throw new Error(`${r} - expected property label (${e.label}) to be a number`)}get label(){return this._label}};var mt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return 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r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return m.dispose(),p.dispose(),{age:u,gender:b,genderProbability:y}}));return n.age.dispose(),n.gender.dispose(),r.isBatchInput?c:c[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){let{params:r,paramMappings:n}=this.extractClassifierParams(e);this._params=r,this._paramMappings=n}extractClassifierParams(e){return ko(e)}extractParamsFromWeightMap(e){let{featureExtractorMap:r,classifierMap:n}=gr(e);return this.faceFeatureExtractor.loadFromWeightMap(r),Bo(n)}extractParams(e){let n=e.slice(0,e.length-1539),a=e.slice(e.length-1539);return this.faceFeatureExtractor.extractWeights(n),this.extractClassifierParams(a)}};var G=v(x());var Fe=class extends Te{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return G.tidy(()=>{let s=(u,f)=>G.stack([G.fill([68],u,"float32"),G.fill([68],f,"float32")],1).as2D(1,136).as1D(),i=(u,f)=>{let{width:l,height:b}=n[u];return f(l,b)?Math.abs(l-b)/2:0},c=u=>i(u,(f,l)=>f<l),m=u=>i(u,(f,l)=>l<f);return t.mul(G.fill([a,136],e,"float32")).sub(G.stack(Array.from(Array(a),(u,f)=>s(c(f),m(f))))).div(G.stack(Array.from(Array(a),(u,f)=>s(n[f].width,n[f].height))))})}forwardInput(t){return G.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await C(t))}async detectLandmarks(t){let e=await C(t),r=G.tidy(()=>G.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(a.dataSync()),c=i.filter((p,u)=>rr(u)),m=i.filter((p,u)=>!rr(u));return new 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g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=v(x());var Cr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Cr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: 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m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=ge(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;T<p;T++)for(let _=0;_<p;_++)for(let E=0;E<u;E++){let W=Ne(F[T][_][E][0]);if(!n||W>n){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=fn;function vo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a*a,0))}var tr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this._distanceThreshold?e:new pe("unknown",e.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version});
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