agent-app/rag-server/node_modules/@vladmandic/face-api/dist/face-api.node-gpu.js

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

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V=v(x());var we=class extends _e{postProcess(t,e,r){let n=r.map(({width:a,height:i})=>{let c=e/Math.max(i,a);return{width:a*c,height:i*c}}),s=n.length;return V.tidy(()=>{let a=(u,f)=>V.stack([V.fill([68],u,"float32"),V.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(V.fill([s,136],e,"float32")).sub(V.stack(Array.from(Array(s),(u,f)=>a(c(f),m(f))))).div(V.stack(Array.from(Array(s),(u,f)=>a(n[f].width,n[f].height))))})}forwardInput(t){return V.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 D(t))}async detectLandmarks(t){let e=await D(t),r=V.tidy(()=>V.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(s,a)=>{let i=Array.from(s.dataSync()),c=i.filter((p,u)=>er(u)),m=i.filter((p,u)=>!er(u));return new 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m=e(a,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let s=be(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}}function rn(o,t,e,r){let{extractWeights:n,getRemainingWeights:s}=k(o),a=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=fa(n,a),p;if(t.withSeparableConvs){let[u,f,l,b,T,h,g,P,y]=r,I=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),j=m(f,l,"conv1"),tt=m(l,b,"conv2"),it=m(b,T,"conv3"),q=m(T,h,"conv4"),Pt=m(h,g,"conv5"),wt=P?m(g,P,"conv6"):void 0,Ft=y?m(P,y,"conv7"):void 0,ie=i(y||P||g,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}else{let[u,f,l,b,T,h,g,P,y]=r,I=c(u,f,"conv0"),j=c(f,l,"conv1"),tt=c(l,b,"conv2"),it=c(b,T,"conv3"),q=c(T,h,"conv4"),Pt=c(h,g,"conv5"),wt=c(g,P,"conv6"),Ft=c(P,y,"conv7"),ie=i(y,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:a}}function la(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}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(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;g<m;g++)for(let P=0;P<m;P++)for(let y=0;y<p;y++){let I=Ne(T[g][P][y][0]);if(!r||I>r){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,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?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(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 ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,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 F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};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 ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=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,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(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?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(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 le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(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=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.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=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(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?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;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,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{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,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.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 me(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 me("unknown",e.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(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=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=No;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});