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

/*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
*/

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ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let 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,g=l?"male":"female",T=l?f:1-f;return m.dispose(),p.dispose(),{age:u,gender:g,genderProbability:T}}));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}=xr(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:g}=n[u];return 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b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(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 Ir=o=>typeof o=="number";function lo(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(!Ir(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: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Ir(t.x)&&Ir(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: 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m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=ge(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=sa(n,s),p;if(t.withSeparableConvs){let[u,f,l,g,T,D,h,_,y]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,g,"conv2"),lt=m(g,T,"conv3"),q=m(T,D,"conv4"),Dt=m(D,h,"conv5"),Et=_?m(h,_,"conv6"):void 0,Mt=y?m(_,y,"conv7"):void 0,$t=i(y||_||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,g,T,D,h,_,y]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,g,"conv2"),lt=c(g,T,"conv3"),q=c(T,D,"conv4"),Dt=c(D,h,"conv5"),Et=c(h,_,"conv6"),Mt=c(_,y,"conv7"),$t=i(y,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: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=Y(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 bo=class extends A{constructor(e){super("TinyYolov2");lo(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)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(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),g=p.map(h=>this.config.classes[h.label]);return Vr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],g[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||bo.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 ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_<p;_++)for(let y=0;y<p;y++)for(let E=0;E<u;E++){let W=Ne(D[_][y][E][0]);if(!n||W>n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:vo,label:yo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*vo,label:yo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}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=bo;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 ca(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 go=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 go.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof go.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 b(1.603231,2.094468),new b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(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 w={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)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),pn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),un=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=un,Pa=mn,wa=pn;var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Mr(Cr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Mr(Cr(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 wt(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}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(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=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(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?w.faceLandmark68TinyNet:w.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)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(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()),we(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=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.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 Fa(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=fn;function xo(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**2,0))}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=>xo(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 Ma(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 we(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 Ia=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});