From 649fa69b3d2473f2332179875cad46610437651d Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Fri, 22 Jan 2021 08:16:24 -0500 Subject: [PATCH] update --- dist/face-api.esm-nobundle.js | 2 +- dist/face-api.esm.js | 2 +- dist/face-api.js | 2 +- dist/face-api.node-gpu.js | 2 +- dist/face-api.node.js | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index f2069ec..c910e2d 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -5,5 +5,5 @@ author: ' */ -var dn=Object.create,Ge=Object.defineProperty,un=Object.getPrototypeOf,fn=Object.prototype.hasOwnProperty,ln=Object.getOwnPropertyNames,hn=Object.getOwnPropertyDescriptor;var lo=o=>Ge(o,"__esModule",{value:!0});var ho=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Er=(o,t)=>{lo(o);for(var e in t)Ge(o,e,{get:t[e],enumerable:!0})},Bt=(o,t,e)=>{if(lo(o),t&&typeof t=="object"||typeof t=="function")for(let r of 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a=l.sigmoid(l.slice(t,[0,0,1],[-1,-1,-1])),s=l.slice(a,[0,0,0],[-1,-1,1]);s=l.reshape(s,[r,s.shape[1]]);let i=l.unstack(n),c=l.unstack(s);return{boxes:i,scores:c}})}var je=b(g());var Oe=b(g());function Ut(o,t){return Oe.tidy(()=>{let e=o.shape[0],r=Oe.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Oe.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function zo(o,t,e){return je.tidy(()=>{let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=Ho(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=zo(a.out,a.conv11,e.prediction_layer);return Go(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*_);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Oo(t)}};function Vo(o){let t=new Xt;return t.extractWeights(o),t}function Jn(o){return Vo(o)}var Uo=class extends Xt{};var Xo=.4,Jo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Zo=[117.001,114.697,97.404],Ko="tiny_yolov2_model",Qo="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(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(!br(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=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function ge(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return 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tn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=qn(n,s),p;if(t.withSeparableConvs){let[d,u,f,v,_,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,f,"conv1"),et=m(f,v,"conv2"),it=m(v,_,"conv3"),X=m(_,h,"conv4"),Pt=m(h,y,"conv5"),_t=T?m(y,T,"conv6"):void 0,wt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}else{let[d,u,f,v,_,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,f,"conv1"),et=c(f,v,"conv2"),it=c(v,_,"conv3"),X=c(_,h,"conv4"),Pt=c(h,y,"conv5"),_t=c(y,T,"conv6"),wt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Zn(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=de(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function en(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Zn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var ft=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ft(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),f=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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 tn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,f]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:te,label:fo}=this.withClassScores?await this.extractPredictedClass(f,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,label:fo,...wt})}}return d.dispose(),u.dispose(),f.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ve=io;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,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?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var He=b(g());var co=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,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 Xt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Pe(this,this.input)}},De=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends po{async run(){let t=await this.parentTask,e=await Jt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(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 Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;function uo(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 Dr=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 xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(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.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>pn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,re as BoundingBox,D as Box,tt as ComposableTask,At as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,Jr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,It as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ne as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,vt as Gender,Ce as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,S as NeuralNetwork,Dt as ObjectDetection,x as Point,go as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as 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vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=We(n,e.fc.age).as1D(),s=We(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=Ho(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=zo(a.out,a.conv11,e.prediction_layer);return Go(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*_);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Oo(t)}};function Vo(o){let t=new Xt;return t.extractWeights(o),t}function Jn(o){return Vo(o)}var Uo=class extends Xt{};var Xo=.4,Jo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Zo=[117.001,114.697,97.404],Ko="tiny_yolov2_model",Qo="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(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(!br(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=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function ge(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),ge(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),ge(e)})}var so=b(g());function qn(o,t){let e=me(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=pe(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function tn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=qn(n,s),p;if(t.withSeparableConvs){let[d,u,f,v,_,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,f,"conv1"),et=m(f,v,"conv2"),it=m(v,_,"conv3"),X=m(_,h,"conv4"),Pt=m(h,y,"conv5"),_t=T?m(y,T,"conv6"):void 0,wt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}else{let[d,u,f,v,_,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,f,"conv1"),et=c(f,v,"conv2"),it=c(v,_,"conv3"),X=c(_,h,"conv4"),Pt=c(h,y,"conv5"),_t=c(y,T,"conv6"),wt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Zn(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=de(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function en(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Zn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var ft=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ft(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),f=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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 tn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,f]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:te,label:fo}=this.withClassScores?await this.extractPredictedClass(f,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,label:fo,...wt})}}return d.dispose(),u.dispose(),f.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ve=io;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,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?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var He=b(g());var co=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,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 Xt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Pe(this,this.input)}},De=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends po{async run(){let t=await this.parentTask,e=await Jt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(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 Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;function uo(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 Dr=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 xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(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.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(o,t){let{width:e,height:r}=new A(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=>pn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,re as BoundingBox,D as Box,tt as ComposableTask,At as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,Jr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,It as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ne as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,vt as Gender,Ce as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,S as NeuralNetwork,Dt as ObjectDetection,x as Point,go as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as TinyFaceDetectorOptions,ye as TinyYolov2,ft as TinyYolov2Options,ba as allFaces,mn as allFacesSsdMobilenetv1,xa as allFacesTinyYolov2,Yr as awaitMediaLoaded,Gr as bufferToImage,ra as computeFaceDescriptor,ae as createCanvas,Le as createCanvasFromMedia,Jn as createFaceDetectionNet,Rn as createFaceRecognitionNet,Vo as createSsdMobilenetv1,ga as createTinyFaceDetector,Kn as createTinyYolov2,wr as detectAllFaces,sn as detectFaceLandmarks,ea as detectFaceLandmarksTiny,la as detectLandmarks,ha as detectSingleFace,Qr as draw,w as env,uo as euclideanDistance,hr as extendWithAge,lr as extendWithFaceDescriptor,jt as extendWithFaceDetection,mr as extendWithFaceExpressions,le as extendWithFaceLandmarks,xr as extendWithGender,ie as extractFaceTensors,se as extractFaces,Mn as fetchImage,Ur as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,zr as imageTensorToCanvas,Vr as imageToSquare,vn as inverseSigmoid,Lr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,qr as isWithFaceExpressions,Vt as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Xr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,kr as minBbox,P as nets,Sr as nonMaxSuppression,ot as normalize,Ar as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Mr as utils,ao as validateConfig,Ta as version}; //# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 82836ee..e128b38 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4045,7 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Please use 'channelsLast'.`);let W=a.makeOutput(h.outShape,"float32"),V=a.dataIdMap.get(W.dataId).id;return v2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,V),W}var Fte={kernelName:Os,backendName:"wasm",setupFunc:Ete,kernelFunc:Ate},$te=!1,Dte=gn(Vo,$te,"bool"),Mte=Un(zs);function Uv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),La({inputs:{x:r},backend:a,attrs:{shape:o}})}var Rte={kernelName:Uo,backendName:"wasm",kernelFunc:Uv};function Pte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Ote={kernelName:mc,backendName:"wasm",kernelFunc:Pte},w2;function Lte(e){w2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function zte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return w2(s,o,l,c,u,i),r}var Bte={kernelName:Ho,backendName:"wasm",kernelFunc:zte,setupFunc:Lte},Wte=Un(Bs),Vte=!1,Ute=gn(Ws,Vte),k2;function Gte(e){k2=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number"])}function Hte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return k2(u,p,d,h,m,r,g),f}var jte={kernelName:Vs,backendName:"wasm",setupFunc:Gte,kernelFunc:Hte},I2;function qte(e){I2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return I2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Xte={kernelName:xi,backendName:"wasm",setupFunc:qte,kernelFunc:Kte},N2;function Yte(e){N2=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Zte={kernelName:vi,backendName:"wasm",setupFunc:Yte,kernelFunc:Jte},T2;function Qte(e){T2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function ene(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=my.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return T2(d,Vn[a.dtype],h,i,p,o,m,f),c}var tne={kernelName:qo,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},S2;function nne(e){S2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ane(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=La({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return S2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var rne={kernelName:jo,backendName:"wasm",setupFunc:nne,kernelFunc:ane},sne=!1,ine=gn(Ko,sne,"bool"),one=!1,lne=gn(Us,one,"bool"),C2;function une(e){C2=e.wasm.cwrap(Gs,null,["number","number","number"])}function cne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;C2(r,n,i)}return s}var pne={kernelName:Gs,backendName:"wasm",setupFunc:une,kernelFunc:cne},dne=!1,hne=gn(Qo,dne,"bool"),mne=!1,fne=gn(el,mne,"bool"),gne=Un(Hs),yne=!1,bne=gn(nl,yne,"bool"),_2;function xne(e){_2=e.wasm.cwrap(js,null,["number, number, number"])}function vne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=gu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;_2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var wne={kernelName:js,backendName:"wasm",setupFunc:xne,kernelFunc:vne},kne=!1,Ine=gn(qs,kne),E2;function Nne(e){E2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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x=E.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Ene={kernelName:Xs,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},F2;function Ane(e){F2=e.wasm.cwrap(Ys,null,["number, number, number"])}function Fne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;F2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var $ne={kernelName:Ys,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},Dne=!1,Mne=gn(Js,Dne),Rne=!0,Pne=gn(Zs,Rne),One=Un(rl);function Gv(e,t){let n=new 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this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Fr?this.faceFeatureExtractor.forwardInput(t):t;return zp(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return iC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=df(t);return this.faceFeatureExtractor.loadFromWeightMap(n),oC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var 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Mr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ai(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Au(n)})}function pse(e,t){let n=Nu(e,t);function a(i,o){let l=tt(e(i)),c=tt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Tu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=pse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function dse(e,t){let n=Gn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function PC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=dse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Tw=class extends sn{constructor(t){super("TinyYolov2");Nw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(to(t,n.conv0,"valid",!1)):Mr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Mr(a,n.conv6):a,a=n.conv7?Mr(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ba(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return Qv(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return PC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Tw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ca(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Fp(g[y][b][v][0]))/c*o,T=(y+Fp(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new bu(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Fu=Tw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:EC,classes:["face"],...t?{anchors:FC,meanRgb:$C}:{anchors:AC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MC:DC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function hse(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var kf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ka=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof z?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function Du(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var OC=.4,LC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],zC=[117.001,114.697,97.404];var Mu=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:OC,classes:["face"],anchors:LC,meanRgb:zC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new ro,tinyFaceDetector:new Mu,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new gf,faceRecognitionNet:new Eu,faceExpressionNet:new hf,ageGenderNet:new ff},BC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),mse=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),fse=(e,t)=>nt.tinyYolov2.locateFaces(e,t),WC=e=>nt.faceLandmark68Net.detectLandmarks(e),gse=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),yse=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),bse=e=>nt.faceExpressionNet.predictExpressions(e),xse=e=>nt.ageGenderNet.predictAgeAndGender(e),VC=e=>nt.ssdMobilenetv1.load(e),vse=e=>nt.tinyFaceDetector.load(e),wse=e=>nt.tinyYolov2.load(e),kse=e=>nt.faceLandmark68Net.load(e),Ise=e=>nt.faceLandmark68TinyNet.load(e),Nse=e=>nt.faceRecognitionNet.load(e),Tse=e=>nt.faceExpressionNet.load(e),Sse=e=>nt.ageGenderNet.load(e),Cse=VC,_se=BC,Ese=WC;var Sw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Sw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>mf(a,n[r]))}withAgeAndGender(){return new Ru(this,this.input)}},Lu=class extends Sw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return mf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},lo=class extends Ou{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Lu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ru=class extends Cw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return xf(vf(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Cw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return xf(vf(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},io=class extends Ru{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Pu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var If=class extends ka{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends If{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>bf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends If{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return bf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var Nf=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},Tf=class extends Nf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof z&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},Sf=class extends Nf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cf=class extends ka{constructor(t,n=new wa){super();this.input=t;this.options=n}},Up=class extends Cf{async run(){let{input:t,options:n}=this,a=n instanceof kf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof wa?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new Tf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ru(this.runAndExtendWithFaceDetections(),this.input)}},_f=class extends Cf{async run(){let t=await new Up(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Sf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function Ase(e,t=new wa){return new _f(e,t)}function Ef(e,t=new wa){return new Up(e,t)}async function UC(e,t){return Ef(e,new wa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Fse(e,t={}){return Ef(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var $se=UC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Af=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Ar)return i;if(i instanceof Float32Array)return new Ar(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Ar(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new $p(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Ar.fromJSON(a));return new Af(n,t.distanceThreshold)}};function Dse(e){let t=new Mu;return t.extractWeights(e),t}function GC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>GC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof vt?e.forSize(n,a):e}var Mse=typeof process!="undefined",Rse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Pse={faceapi:lC,node:Mse,browser:Rse};export{ff as AgeGenderNet,bu as BoundingBox,ct as Box,ka as ComposableTask,ys as ComputeAllFaceDescriptorsTask,If as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,Tf as DetectAllFaceLandmarksTask,Up as DetectAllFacesTask,Nf as DetectFaceLandmarksTaskBase,Cf as DetectFacesTaskBase,Sf as DetectSingleFaceLandmarksTask,_f as DetectSingleFaceTask,yn as Dimensions,mw as FACE_EXPRESSION_LABELS,vt as FaceDetection,_C as FaceDetectionNet,hf as FaceExpressionNet,gs as FaceExpressions,_u as FaceLandmark68Net,gf as FaceLandmark68TinyNet,gC as FaceLandmarkNet,ra as FaceLandmarks,eC as FaceLandmarks5,vu as FaceLandmarks68,$p as FaceMatch,Af as FaceMatcher,Eu as FaceRecognitionNet,$r as Gender,Dp as LabeledBox,Ar as LabeledFaceDescriptors,Fr as NetInput,sn as NeuralNetwork,ms as ObjectDetection,De as Point,tC as PredictedBox,xu as Rect,ro as SsdMobilenetv1,wa as SsdMobilenetv1Options,Mu as TinyFaceDetector,kf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,$se as allFaces,UC as allFacesSsdMobilenetv1,Fse as allFacesTinyYolov2,lw as awaitMediaLoaded,uw as bufferToImage,yse as computeFaceDescriptor,wu as createCanvas,Pp as createCanvasFromMedia,cse as createFaceDetectionNet,Qre as createFaceRecognitionNet,CC as createSsdMobilenetv1,Dse as createTinyFaceDetector,hse as createTinyYolov2,Ef as detectAllFaces,WC as detectFaceLandmarks,gse as detectFaceLandmarksTiny,Ese as detectLandmarks,Ase as detectSingleFace,bw as draw,it as env,_w as euclideanDistance,xf as extendWithAge,bf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,mf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,vf as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,Ure as fetchImage,dw as fetchJson,Gre as fetchNetWeights,eo as fetchOrThrow,Cn as getContext2dOrThrow,Qi as getMediaDimensions,cw as imageTensorToCanvas,pw as imageToSquare,Mre as inverseSigmoid,Jv as iou,af as isMediaElement,Rp as isMediaLoaded,ese as isWithAge,or as isWithFaceDetection,fw as isWithFaceExpressions,no as isWithFaceLandmarks,tse as isWithGender,Sse as loadAgeGenderModel,Cse as loadFaceDetectionModel,Tse as loadFaceExpressionModel,kse as loadFaceLandmarkModel,Ise as loadFaceLandmarkTinyModel,Nse as loadFaceRecognitionModel,VC as loadSsdMobilenetv1Model,vse as loadTinyFaceDetectorModel,wse as loadTinyYolov2Model,hw as loadWeightMap,_se as locateFaces,Hre as matchDimensions,Zv as minBbox,nt as nets,Qv as nonMaxSuppression,Ba as normalize,ew as padToSquare,xse as predictAgeAndGender,bse as recognizeFaceExpressions,GC as resizeResults,Zi as resolveInput,Dre as shuffleArray,Fp as sigmoid,BC as ssdMobilenetv1,Bg as tf,mse as tinyFaceDetector,fse as tinyYolov2,yt as toNetInput,qv as utils,Nw as validateConfig,Pse as version}; + `}};function Dee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=eh(r.dtype),g=(x,N,T,C,$)=>{let F=x.shape[0],O=x.shape[1],W=E.segment_util.segOpComputeOptimalWindowSize(O,$),V={windowSize:W,inSize:O,batchSize:F,numSegments:$},H=new $ee(V,N),K=n.compileAndRun(H,[x,T],C);if(l.push(K),K.shape[1]===$)return K;let j=a2({backend:n,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),Y=i2({inputs:{x:j},backend:n,attrs:{reps:[O/W]}});return l.push(j),l.push(Y),g(K,N,Y,C,$)},y=g(m,"unsortedSegmentSum",s,f,i),b=ve({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=E.getUndoAxesPermutation(u);v=Sn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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Cp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(Cp||(Cp={}));var o2;function Lee(e){o2=e.wasm.cwrap(bi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function zee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let $=n.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);m=$.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Cp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let 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Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Zte={kernelName:vi,backendName:"wasm",setupFunc:Yte,kernelFunc:Jte},T2;function Qte(e){T2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function ene(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=my.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return T2(d,Vn[a.dtype],h,i,p,o,m,f),c}var tne={kernelName:qo,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},S2;function nne(e){S2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ane(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=La({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return S2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var rne={kernelName:jo,backendName:"wasm",setupFunc:nne,kernelFunc:ane},sne=!1,ine=gn(Ko,sne,"bool"),one=!1,lne=gn(Us,one,"bool"),C2;function une(e){C2=e.wasm.cwrap(Gs,null,["number","number","number"])}function cne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;C2(r,n,i)}return s}var pne={kernelName:Gs,backendName:"wasm",setupFunc:une,kernelFunc:cne},dne=!1,hne=gn(Qo,dne,"bool"),mne=!1,fne=gn(el,mne,"bool"),gne=Un(Hs),yne=!1,bne=gn(nl,yne,"bool"),_2;function xne(e){_2=e.wasm.cwrap(js,null,["number, number, number"])}function vne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=gu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;_2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var wne={kernelName:js,backendName:"wasm",setupFunc:xne,kernelFunc:vne},kne=!1,Ine=gn(qs,kne),E2;function Nne(e){E2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),$=a.dataIdMap.get(C.dataId).id;return E2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,$),C}var Sne={kernelName:Ks,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},A2;function Cne(e){A2=e.wasm.cwrap(Xs,null,["number, number, number"])}function _ne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Ym({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;A2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=E.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Ene={kernelName:Xs,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},F2;function Ane(e){F2=e.wasm.cwrap(Ys,null,["number, number, number"])}function Fne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;F2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var $ne={kernelName:Ys,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},Dne=!1,Mne=gn(Js,Dne),Rne=!0,Pne=gn(Zs,Rne),One=Un(rl);function Gv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var $2;function Lne(e){$2=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function zne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=$2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Gv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Bne={kernelName:il,backendName:"wasm",setupFunc:Lne,kernelFunc:zne},D2;function Wne(e){D2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","bool"])}function Vne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=D2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Gv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Une={kernelName:ol,backendName:"wasm",setupFunc:Wne,kernelFunc:Vne},M2;function Gne(e){M2=e.wasm.cwrap(ll,"number",["number","number","number","number","number","number"])}function Hne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=M2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Gv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var jne={kernelName:ll,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},qne=!1,Kne=gn(sl,qne,"bool"),R2;function Xne(e){R2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Yne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return R2(u,s,i,o,c),l}var Jne={kernelName:Qs,backendName:"wasm",setupFunc:Xne,kernelFunc:Yne};function Zne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Qne={kernelName:ul,backendName:"wasm",kernelFunc:Zne};function eae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Uv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching 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lae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return O2(s,i,l),o}var uae={kernelName:ni,backendName:"wasm",setupFunc:oae,kernelFunc:lae},L2;function cae(e){L2=e.wasm.cwrap(pl,null,["number","number","number","number"])}function pae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;L2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var dae={kernelName:pl,backendName:"wasm",setupFunc:cae,kernelFunc:pae},hae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=fv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},mae={kernelName:vc,backendName:"wasm",kernelFunc:hae},fae=!0,gae=gn(Ls,fae),yae=Un(ai),bae=Un(si),z2;function xae(e){z2=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number"])}function vae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Ym({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return z2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var 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De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ct({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ct({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return an&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),c<1&&(l=2-c,c=1),u<1&&(l=2-u,u=1),{dy:i,edy:l,dx:s,edx:o,y:u,ey:d,x:c,ex:p,w:a,h:r}}calibrate(t){return new ct({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var bu=class extends ct{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var ms=class{constructor(t,n,a,r,s){this._imageDims=new yn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ct(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ct(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new ms(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var vt=class extends ms{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new vt(a,r,s)}};function Jv(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function Zv(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let 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t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Ore),l=Yi(t),c=Math.floor(Math.max(0,l.x-Rre*o)),u=Math.floor(Math.max(0,l.y-Pre*o));return new xu(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=Zv(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var eC=class extends ra{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Yi([t[3],t[4]])]}};var vu=class extends ra{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return 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this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Ar(t.label,n)}};var tC=class extends Dp{static assertIsValidPredictedBox(t,n){if(Dp.assertIsValidLabeledBox(t,n),!yu(t.score)||!yu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function or(e){return e.detection instanceof vt}function Ji(e,t){return{...e,...{detection:t}}}function tw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");let t=()=>{throw new Error("readFile - filesystem not available for browser 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lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Mp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},fs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof fs?t.text:t,this.anchor=n,this.options=new Mp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var ow=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Mp({...i,...s})}},nf=class{constructor(t,n={}){this.box=new ct(t),this.options=new ow(n)}draw(t){let n=Cn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new fs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Vre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof vt?a.score:or(a)?a.detection.score:void 0,s=a instanceof vt?a.box:or(a)?a.detection.box:new ct(a),i=r?`${Xi(r)}`:void 0;new nf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=it.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function lw(e){return new Promise((t,n)=>{if(e instanceof it.getEnv().Canvas||Rp(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function uw(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=it.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function Qi(e){let{Image:t,Video:n}=it.getEnv();return e instanceof t?new yn(e.naturalWidth,e.naturalHeight):e instanceof n?new yn(e.videoWidth,e.videoHeight):new yn(e.width,e.height)}function wu({width:e,height:t}){let{createCanvasElement:n}=it.getEnv(),a=n();return a.width=e,a.height=t,a}function Pp(e,t){let{ImageData:n}=it.getEnv();if(!(e instanceof n)&&!Rp(e))throw new Error("createCanvasFromMedia - media has not finished 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n=this.getInputWidth(t),a=this.getInputHeight(t);return Yv({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,D(()=>{let a=ir(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof z){let o=aa(i)?i:i.expandDims();return o=ew(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ja.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof it.getEnv().Canvas)return Ni.fromPixels(pw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>pe(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function yt(e){if(e instanceof Fr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(Zi);return a.forEach((r,s)=>{if(!af(r)&&!Er(r)&&!aa(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve 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lse(e){let t=ht(Ue(e,[1,0])),n=[ge(t[2],t[0]),ge(t[3],t[1])],a=[Z(t[0],we(n[0],he(2))),Z(t[1],we(n[1],he(2)))];return{sizes:n,centers:a}}function use(e,t){let{sizes:n,centers:a}=lse(e),r=ht(Ue(t,[1,0])),s=we(L(dn(we(r[2],he(5))),n[0]),he(2)),i=Z(L(we(r[0],he(10)),n[0]),a[0]),o=we(L(dn(we(r[3],he(5))),n[1]),he(2)),l=Z(L(we(r[1],he(10)),n[1]),a[1]);return Ue(Dt([ge(i,s),ge(l,o),Z(i,s),Z(l,o)]),[1,0])}function TC(e,t,n){return D(()=>{let a=e.shape[0],r=use(q(qa(n.extra_dim,[a,1,1]),[-1,4]),q(e,[-1,4]));r=q(r,[a,r.shape[0]/a,4]);let s=da(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=q(i,[a,i.shape[1]]);let o=ht(r),l=ht(i);return{boxes:o,scores:l}})}function ao(e,t){return D(()=>{let n=e.shape[0],a=q(to(e,t.box_encoding_predictor),[n,-1,1,4]),r=q(to(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function SC(e,t,n){return D(()=>{let a=va(e,n.conv_0,[1,1]),r=va(a,n.conv_1,[2,2]),s=va(r,n.conv_2,[1,1]),i=va(s,n.conv_3,[2,2]),o=va(i,n.conv_4,[1,1]),l=va(o,n.conv_5,[2,2]),c=va(l,n.conv_6,[1,1]),u=va(c,n.conv_7,[2,2]),p=ao(t,n.box_predictor_0),d=ao(e,n.box_predictor_1),h=ao(r,n.box_predictor_2),m=ao(i,n.box_predictor_3),f=ao(l,n.box_predictor_4),g=ao(u,n.box_predictor_5),y=Qe([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var wa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ro=class extends sn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(512,!1),"float32"),r=ge(L(a,he(.007843137718737125)),he(1)),s=IC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=SC(s.out,s.conv11,n.prediction_layer);return TC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await yt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new wa(n),s=await yt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map($=>$*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map($=>$*f);return new vt(u[v],new xu(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return kC(t)}extractParams(t){return wC(t)}};function CC(e){let t=new ro;return t.extractWeights(e),t}function cse(e){return CC(e)}var _C=class extends ro{};var EC=.4,AC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],FC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],$C=[117.001,114.697,97.404],DC="tiny_yolov2_model",MC="tiny_yolov2_separable_conv_model";var wf=e=>typeof e=="number";function Nw(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!wf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>wf(t.x)&&wf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(wf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Au(e){return D(()=>{let t=L(e,he(.10000000149011612));return Z(Ye(ge(e,t)),t)})}function Dr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=ge(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Au(n)})}function Mr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ai(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Au(n)})}function pse(e,t){let n=Nu(e,t);function a(i,o){let l=tt(e(i)),c=tt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Tu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=pse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function dse(e,t){let n=Gn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function PC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=dse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Tw=class extends sn{constructor(t){super("TinyYolov2");Nw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(to(t,n.conv0,"valid",!1)):Mr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Mr(a,n.conv6):a,a=n.conv7?Mr(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ba(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return Qv(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return PC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Tw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ca(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Fp(g[y][b][v][0]))/c*o,T=(y+Fp(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new bu(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Fu=Tw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:EC,classes:["face"],...t?{anchors:FC,meanRgb:$C}:{anchors:AC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MC:DC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function hse(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var kf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ka=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof z?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function Du(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var OC=.4,LC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],zC=[117.001,114.697,97.404];var Mu=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:OC,classes:["face"],anchors:LC,meanRgb:zC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new ro,tinyFaceDetector:new Mu,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new gf,faceRecognitionNet:new Eu,faceExpressionNet:new hf,ageGenderNet:new ff},BC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),mse=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),fse=(e,t)=>nt.tinyYolov2.locateFaces(e,t),WC=e=>nt.faceLandmark68Net.detectLandmarks(e),gse=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),yse=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),bse=e=>nt.faceExpressionNet.predictExpressions(e),xse=e=>nt.ageGenderNet.predictAgeAndGender(e),VC=e=>nt.ssdMobilenetv1.load(e),vse=e=>nt.tinyFaceDetector.load(e),wse=e=>nt.tinyYolov2.load(e),kse=e=>nt.faceLandmark68Net.load(e),Ise=e=>nt.faceLandmark68TinyNet.load(e),Nse=e=>nt.faceRecognitionNet.load(e),Tse=e=>nt.faceExpressionNet.load(e),Sse=e=>nt.ageGenderNet.load(e),Cse=VC,_se=BC,Ese=WC;var Sw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Sw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>mf(a,n[r]))}withAgeAndGender(){return new Ru(this,this.input)}},Lu=class extends Sw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return mf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},lo=class extends Ou{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Lu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ru=class extends Cw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return xf(vf(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Cw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return xf(vf(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},io=class extends Ru{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Pu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var If=class extends ka{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends If{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>bf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends If{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return bf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var Nf=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},Tf=class extends Nf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof z&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},Sf=class extends Nf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cf=class extends ka{constructor(t,n=new wa){super();this.input=t;this.options=n}},Up=class extends Cf{async run(){let{input:t,options:n}=this,a=n instanceof kf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof wa?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new Tf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ru(this.runAndExtendWithFaceDetections(),this.input)}},_f=class extends Cf{async run(){let t=await new Up(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Sf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function Ase(e,t=new wa){return new _f(e,t)}function Ef(e,t=new wa){return new Up(e,t)}async function UC(e,t){return Ef(e,new wa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Fse(e,t={}){return Ef(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var $se=UC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Af=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Ar)return i;if(i instanceof Float32Array)return new Ar(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Ar(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new $p(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Ar.fromJSON(a));return new Af(n,t.distanceThreshold)}};function Dse(e){let t=new Mu;return t.extractWeights(e),t}function GC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>GC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof vt?e.forSize(n,a):e}var Mse=typeof process!="undefined",Rse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Pse={faceapi:lC,node:Mse,browser:Rse};export{ff as AgeGenderNet,bu as BoundingBox,ct as Box,ka as ComposableTask,ys as ComputeAllFaceDescriptorsTask,If as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,Tf as DetectAllFaceLandmarksTask,Up as DetectAllFacesTask,Nf as DetectFaceLandmarksTaskBase,Cf as DetectFacesTaskBase,Sf as DetectSingleFaceLandmarksTask,_f as DetectSingleFaceTask,yn as Dimensions,mw as FACE_EXPRESSION_LABELS,vt as FaceDetection,_C as FaceDetectionNet,hf as FaceExpressionNet,gs as FaceExpressions,_u as FaceLandmark68Net,gf as FaceLandmark68TinyNet,gC as FaceLandmarkNet,ra as FaceLandmarks,eC as FaceLandmarks5,vu as FaceLandmarks68,$p as FaceMatch,Af as FaceMatcher,Eu as FaceRecognitionNet,$r as Gender,Dp as LabeledBox,Ar as LabeledFaceDescriptors,Fr as NetInput,sn as NeuralNetwork,ms as ObjectDetection,De as Point,tC as PredictedBox,xu as Rect,ro as SsdMobilenetv1,wa as SsdMobilenetv1Options,Mu as TinyFaceDetector,kf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,$se as allFaces,UC as allFacesSsdMobilenetv1,Fse as allFacesTinyYolov2,lw as awaitMediaLoaded,uw as bufferToImage,yse as computeFaceDescriptor,wu as createCanvas,Pp as createCanvasFromMedia,cse as createFaceDetectionNet,Qre as createFaceRecognitionNet,CC as createSsdMobilenetv1,Dse as createTinyFaceDetector,hse as createTinyYolov2,Ef as detectAllFaces,WC as detectFaceLandmarks,gse as detectFaceLandmarksTiny,Ese as detectLandmarks,Ase as detectSingleFace,bw as draw,it as env,_w as euclideanDistance,xf as extendWithAge,bf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,mf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,vf as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,Ure as fetchImage,dw as fetchJson,Gre as fetchNetWeights,eo as fetchOrThrow,Cn as getContext2dOrThrow,Qi as getMediaDimensions,cw as imageTensorToCanvas,pw as imageToSquare,Mre as inverseSigmoid,Jv as iou,af as isMediaElement,Rp as isMediaLoaded,ese as isWithAge,or as isWithFaceDetection,fw as isWithFaceExpressions,no as isWithFaceLandmarks,tse as isWithGender,Sse as loadAgeGenderModel,Cse as loadFaceDetectionModel,Tse as loadFaceExpressionModel,kse as loadFaceLandmarkModel,Ise as loadFaceLandmarkTinyModel,Nse as loadFaceRecognitionModel,VC as loadSsdMobilenetv1Model,vse as loadTinyFaceDetectorModel,wse as loadTinyYolov2Model,hw as loadWeightMap,_se as locateFaces,Hre as matchDimensions,Zv as minBbox,nt as nets,Qv as nonMaxSuppression,Ba as normalize,ew as padToSquare,xse as predictAgeAndGender,bse as recognizeFaceExpressions,GC as resizeResults,Zi as resolveInput,Dre as shuffleArray,Fp as sigmoid,BC as ssdMobilenetv1,Bg as tf,mse as tinyFaceDetector,fse as tinyYolov2,yt as toNetInput,qv as utils,Nw as validateConfig,Pse as version}; /** * @license * Copyright 2017 Google LLC. 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Please use 'channelsLast'.`);let W=a.makeOutput(h.outShape,"float32"),V=a.dataIdMap.get(W.dataId).id;return A2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,V),W}var lne={kernelName:Us,backendName:"wasm",setupFunc:ine,kernelFunc:one},une=!1,cne=yn(Qo,une,"bool"),pne=Un(Hs);function sw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),za({inputs:{x:r},backend:a,attrs:{shape:o}})}var dne={kernelName:el,backendName:"wasm",kernelFunc:sw};function hne(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var mne={kernelName:wc,backendName:"wasm",kernelFunc:hne},F2;function fne(e){F2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function gne(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return F2(s,o,l,c,u,i),r}var yne={kernelName:nl,backendName:"wasm",kernelFunc:gne,setupFunc:fne},bne=Un(js),xne=!1,vne=yn(qs,xne),$2;function wne(e){$2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number"])}function kne(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return $2(u,p,d,h,m,r,g),f}var Ine={kernelName:Ks,backendName:"wasm",setupFunc:wne,kernelFunc:kne},D2;function Nne(e){D2=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Mp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return D2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Sne={kernelName:Ti,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},M2;function Cne(e){M2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _ne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Mp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return M2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Ene={kernelName:Si,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},R2;function Ane(e){R2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function Fne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ay.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return R2(d,Vn[a.dtype],h,i,p,o,m,f),c}var $ne={kernelName:rl,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},P2;function Dne(e){P2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Mne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=za({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=za({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return P2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var Rne={kernelName:al,backendName:"wasm",setupFunc:Dne,kernelFunc:Mne},Pne=!1,One=yn(sl,Pne,"bool"),Lne=!1,zne=yn(Xs,Lne,"bool"),O2;function Bne(e){O2=e.wasm.cwrap(Ys,null,["number","number","number"])}function Wne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;O2(r,n,i)}return s}var Vne={kernelName:Ys,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},Une=!1,Gne=yn(cl,Une,"bool"),Hne=!1,jne=yn(pl,Hne,"bool"),qne=Un(Js),Kne=!1,Xne=yn(hl,Kne,"bool"),L2;function Yne(e){L2=e.wasm.cwrap(Zs,null,["number, number, number"])}function Jne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Cu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;L2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Zne={kernelName:Zs,backendName:"wasm",setupFunc:Yne,kernelFunc:Jne},Qne=!1,eae=yn(Qs,Qne),z2;function tae(e){z2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nae(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t;return Vp(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return xC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Bf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),vC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var 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Fse(e){let t=ht(Ue(e,[1,0])),n=[ge(t[2],t[0]),ge(t[3],t[1])],a=[Z(t[0],we(n[0],he(2))),Z(t[1],we(n[1],he(2)))];return{sizes:n,centers:a}}function $se(e,t){let{sizes:n,centers:a}=Fse(e),r=ht(Ue(t,[1,0])),s=we(L(hn(we(r[2],he(5))),n[0]),he(2)),i=Z(L(we(r[0],he(10)),n[0]),a[0]),o=we(L(hn(we(r[3],he(5))),n[1]),he(2)),l=Z(L(we(r[1],he(10)),n[1]),a[1]);return Ue(Dt([ge(i,s),ge(l,o),Z(i,s),Z(l,o)]),[1,0])}function BC(e,t,n){return D(()=>{let a=e.shape[0],r=$se(q(Xa(n.extra_dim,[a,1,1]),[-1,4]),q(e,[-1,4]));r=q(r,[a,r.shape[0]/a,4]);let s=ma(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=q(i,[a,i.shape[1]]);let o=ht(r),l=ht(i);return{boxes:o,scores:l}})}function mo(e,t){return D(()=>{let n=e.shape[0],a=q(uo(e,t.box_encoding_predictor),[n,-1,1,4]),r=q(uo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function WC(e,t,n){return D(()=>{let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ns=class extends tn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(512,!1),"float32"),r=ge(L(a,he(.007843137718737125)),he(1)),s=LC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=WC(s.out,s.conv11,n.prediction_layer);return BC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await yt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await yt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map($=>$*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map($=>$*f);return new bt(u[v],new ro(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return OC(t)}extractParams(t){return PC(t)}};function Ew(e){let t=new Ns;return t.extractWeights(e),t}function VC(e){return Ew(e)}var Aw=class extends Ns{};var UC=.4,GC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],HC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],jC=[117.001,114.697,97.404],qC="tiny_yolov2_model",KC="tiny_yolov2_separable_conv_model";var Hf=e=>typeof e=="number";function jf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Hf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Hf(t.x)&&Hf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Hf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,he(.10000000149011612));return Z(Ye(ge(e,t)),t)})}function Rr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=ge(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Pr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function Dse(e,t){let n=Mu(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Ru(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function XC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Dse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Mse(e,t){let n=Hn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function YC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Mse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ua=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Fw=class extends tn{constructor(t){super("TinyYolov2");jf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Rr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Rr(a,n.conv6),a=Rr(a,n.conv7),uo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(uo(t,n.conv0,"valid",!1)):Pr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Pr(a,n.conv6):a,a=n.conv7?Pr(a,n.conv7):a,uo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?ka(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ua(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return If(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Dr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return YC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Fw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return XC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?_a(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new ao(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Lu=Fw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var fo=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:UC,classes:["face"],...t?{anchors:HC,meanRgb:jC}:{anchors:GC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return 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De(9.041765,10.66308)],e_=[117.001,114.697,97.404];var yo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:ZC,classes:["face"],anchors:QC,meanRgb:e_,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Ns,tinyFaceDetector:new yo,tinyYolov2:new fo,faceLandmark68Net:new po,faceLandmark68TinyNet:new Kp,faceRecognitionNet:new ho,faceExpressionNet:new Gp,ageGenderNet:new jp},$w=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),t_=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),n_=(e,t)=>et.tinyYolov2.locateFaces(e,t),Dw=e=>et.faceLandmark68Net.detectLandmarks(e),a_=e=>et.faceLandmark68TinyNet.detectLandmarks(e),r_=e=>et.faceRecognitionNet.computeFaceDescriptor(e),s_=e=>et.faceExpressionNet.predictExpressions(e),i_=e=>et.ageGenderNet.predictAgeAndGender(e),Mw=e=>et.ssdMobilenetv1.load(e),o_=e=>et.tinyFaceDetector.load(e),l_=e=>et.tinyYolov2.load(e),u_=e=>et.faceLandmark68Net.load(e),c_=e=>et.faceLandmark68TinyNet.load(e),p_=e=>et.faceRecognitionNet.load(e),d_=e=>et.faceExpressionNet.load(e),h_=e=>et.ageGenderNet.load(e),m_=Mw,f_=$w,g_=Dw;var Rw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Rw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Hp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Rw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Hp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},vo=class extends Vu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},wo=class extends Uu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var Pw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Pw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Jp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Jp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},bo=class extends Bu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},xo=class extends Wu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var ed=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Or=class extends ed{async run(){let t=await this.parentTask;return(await go(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Yp(t[r],a))}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}},Lr=class extends ed{async run(){let t=await 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Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return M2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Ene={kernelName:Si,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},R2;function Ane(e){R2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function Fne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ay.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return R2(d,Vn[a.dtype],h,i,p,o,m,f),c}var $ne={kernelName:rl,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},P2;function Dne(e){P2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Mne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=za({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=za({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return P2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var Rne={kernelName:al,backendName:"wasm",setupFunc:Dne,kernelFunc:Mne},Pne=!1,One=yn(sl,Pne,"bool"),Lne=!1,zne=yn(Xs,Lne,"bool"),O2;function Bne(e){O2=e.wasm.cwrap(Ys,null,["number","number","number"])}function Wne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;O2(r,n,i)}return s}var Vne={kernelName:Ys,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},Une=!1,Gne=yn(cl,Une,"bool"),Hne=!1,jne=yn(pl,Hne,"bool"),qne=Un(Js),Kne=!1,Xne=yn(hl,Kne,"bool"),L2;function Yne(e){L2=e.wasm.cwrap(Zs,null,["number, number, number"])}function Jne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Cu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;L2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Zne={kernelName:Zs,backendName:"wasm",setupFunc:Yne,kernelFunc:Jne},Qne=!1,eae=yn(Qs,Qne),z2;function tae(e){z2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nae(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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x=E.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var iae={kernelName:ti,backendName:"wasm",setupFunc:rae,kernelFunc:sae},W2;function oae(e){W2=e.wasm.cwrap(ni,null,["number, number, number"])}function lae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;W2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var uae={kernelName:ni,backendName:"wasm",setupFunc:oae,kernelFunc:lae},cae=!1,pae=yn(ai,cae),dae=!0,hae=yn(ri,dae),mae=Un(fl);function iw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var V2;function fae(e){V2=e.wasm.cwrap(yl,"number",["number","number","number","number","number"])}function gae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=V2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=iw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var yae={kernelName:yl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},U2;function bae(e){U2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","bool"])}function xae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=U2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=iw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var vae={kernelName:bl,backendName:"wasm",setupFunc:bae,kernelFunc:xae},G2;function wae(e){G2=e.wasm.cwrap(xl,"number",["number","number","number","number","number","number"])}function kae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=G2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=iw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Iae={kernelName:xl,backendName:"wasm",setupFunc:wae,kernelFunc:kae},Nae=!1,Tae=yn(gl,Nae,"bool"),H2;function Sae(e){H2=e.wasm.cwrap(si,null,["number","number","number","number","number"])}function Cae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return H2(u,s,i,o,c),l}var _ae={kernelName:si,backendName:"wasm",setupFunc:Sae,kernelFunc:Cae};function Eae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Aae={kernelName:vl,backendName:"wasm",kernelFunc:Eae};function Fae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return sw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching 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zae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return q2(s,i,l),o}var Bae={kernelName:li,backendName:"wasm",setupFunc:Lae,kernelFunc:zae},K2;function Wae(e){K2=e.wasm.cwrap(kl,null,["number","number","number","number"])}function Vae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;K2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Uae={kernelName:kl,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},Gae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Fv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Hae={kernelName:Cc,backendName:"wasm",kernelFunc:Gae},jae=!0,qae=yn(Gs,jae),Kae=Un(ui),Xae=Un(pi),X2;function Yae(e){X2=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number"])}function Jae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=ff({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return X2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Zae={kernelName:ci,backendName:"wasm",setupFunc:Yae,kernelFunc:Jae},Y2;function Qae(e){Y2=e.wasm.cwrap(di,null,["number","array","number","array","number","number"])}function ere(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return hf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);return Y2(l,u,i.length,p,r.shape.length,c),za({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var tre={kernelName:di,backendName:"wasm",kernelFunc:ere,setupFunc:Qae},J2;function nre(e){J2=e.wasm.cwrap(Ll,null,["number","number","number","number","number","number","number","number","array","number","number"])}function are(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return J2(c,p,d,h,m,s,f,g,x,v.length,u),l}var rre={kernelName:Ll,backendName:"wasm",kernelFunc:are,setupFunc:nre},sre=Un(hi),ire=Un(mi),Z2;function ore(e){Z2=e.wasm.cwrap(Tl,null,["number","number","number","number","number","number","array","number","number"])}function lre(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=Fy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return Z2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var ure={kernelName:Tl,backendName:"wasm",setupFunc:ore,kernelFunc:lre},Q2;function cre(e){Q2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function pre(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:k.sizeFromShape(r.shape.slice(1));return Q2(i,o,l,h,u),c}var dre={kernelName:Sl,backendName:"wasm",kernelFunc:pre,setupFunc:cre},eC;function hre(e){eC=e.wasm.cwrap(gi,null,["number","number"])}function mre(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||eC(a,s),r}var fre={kernelName:"Sigmoid",backendName:"wasm",setupFunc:hre,kernelFunc:mre},gre=Un(fi);function gf(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=dn.parseSliceParams(t,n,a),o=dn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),p=r.dataIdMap.get(c.dataId);if(o){let m=dn.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(c).set(l.subarray(m,m+k.sizeFromShape(i))),c}if(t.dtype==="string"){let m=Km(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)yre(l,u[0],d,s,i);else if(h===3)bre(l,u[0],u[1],d,s,i);else if(h===4)xre(l,u[0],u[1],u[2],d,s,i);else{let m=Km(l,s,i,t.shape,t.dtype);d.set(m)}return c}function yre(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let c=i;c{let d=[...u];d[o]=p;let h=gf({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return c[o]+=p,h})}var Tre={kernelName:$l,backendName:"wasm",kernelFunc:Nre},Sre=Un(yi),Cre=Un(Ac),_re=!0,Ere=yn(vi,_re),nC;function 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un(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof bt?t.box.floor():new lt(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/dse),l=no(t),c=Math.floor(Math.max(0,l.x-cse*o)),u=Math.floor(Math.max(0,l.y-pse*o));return new ro(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=kf(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var dw=class extends Gn{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],no([t[3],t[4]])]}};var so=class extends Gn{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(no)}};var Au=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${to(this.distance)})`:""}`}};var Fu=class extends lt{static assertIsValidLabeledBox(t,n){if(lt.assertIsValidBox(t,n),!Ba(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var ur=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new ur(t.label,n)}};var hw=class extends Fu{static assertIsValidPredictedBox(t,n){if(Fu.assertIsValidLabeledBox(t,n),!_u(t.score)||!_u(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function Wa(e){return e.detection instanceof bt}function bs(e,t){return{...e,...{detection:t}}}function mw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");let t=()=>{throw new Error("readFile - filesystem not available for browser 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isBrowser()");let{Canvas:t=en.Canvas,Image:n=en.Image}=e;en.Canvas=t,en.Image=n,en.createCanvasElement=e.createCanvasElement||(()=>new t),en.createImageElement=e.createImageElement||(()=>new n),en.ImageData=e.ImageData||en.ImageData,en.Video=e.Video||en.Video,en.fetch=e.fetch||en.fetch,en.readFile=e.readFile||en.readFile}var st={getEnv:fse,setEnv:bw,initialize:xw,createBrowserEnv:mw,createFileSystem:Tf,createNodejsEnv:fw,monkeyPatch:gse,isBrowser:gw,isNodejs:yw.isNodejs};xw();function xs(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function bn(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=xs(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var cr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(cr||(cr={}));var Lp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||cr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},vs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof vs?t.text:t,this.anchor=n,this.options=new Lp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var vw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:cr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Lp({...i,...s})}},Sf=class{constructor(t,n={}){this.box=new lt(t),this.options=new vw(n)}draw(t){let n=bn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new vs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function yse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:Wa(a)?a.detection.score:void 0,s=a instanceof bt?a.box:Wa(a)?a.detection.box:new lt(a),i=r?`${to(r)}`:void 0;new Sf(s,{label:i}).draw(e)})}function $u(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Cf(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||$u(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function _f(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function ws(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new un(e.naturalWidth,e.naturalHeight):e instanceof n?new un(e.videoWidth,e.videoHeight):new un(e.width,e.height)}function io({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Du(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!$u(e))throw new Error("createCanvasFromMedia - media has not finished 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n=this.getInputWidth(t),a=this.getInputHeight(t);return pw({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,D(()=>{let a=lr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof z){let o=ra(i)?i:i.expandDims();return o=Nf(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qa.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return Ai.fromPixels(Af(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>pe(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function yt(e){if(e instanceof pr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(xs);return a.forEach((r,s)=>{if(!zp(r)&&!$r(r)&&!ra(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve 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a=_n(a,t.separable_conv0,[1,1]),a=_n(Ye(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=Z(a,NC(e,t.expansion_conv,[2,2])),a}function kse(e,t){let n=_n(Ye(e),t.separable_conv0,[1,1]);return n=_n(Ye(n),t.separable_conv1,[1,1]),n=_n(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Nw=class extends tn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=ka(a,[122.782,117.001,104.298]).div(he(256)),i=Ye(NC(s,n.entry_flow.conv_in,[2,2]));return i=Iw(i,n.entry_flow.reduction_block_0,!1),i=Iw(i,n.entry_flow.reduction_block_1),lr(this._numMainBlocks,0,1).forEach(o=>{i=kse(i,n.middle_flow[`main_block_${o}`])}),i=Iw(i,n.exit_flow.reduction_block),i=Ye(_n(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await yt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return IC(t,this._numMainBlocks)}extractParams(t){return kC(t,this._numMainBlocks)}};function TC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=Rf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function SC(e){let t=[],n=Hn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var dr;(function(e){e.FEMALE="female",e.MALE="male"})(dr||(dr={}));var jp=class extends tn{constructor(t=new Nw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t,r=Jn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Vp(r,n.fc.age).as1D(),i=Vp(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return D(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:_a(a)}})}async forward(t){return this.forwardInput(await yt(t))}async predictAgeAndGender(t){let n=await yt(t),a=await this.forwardInput(n),r=ht(a.age),s=ht(a.gender),i=r.map((l,c)=>({ageTensor:l,genderTensor:s[c]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:c})=>{let u=(await l.data())[0],p=(await c.data())[0],d=p>.5,h=d?dr.MALE:dr.FEMALE,m=d?p:1-p;return l.dispose(),c.dispose(),{age:u,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return TC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Bf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),SC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var qp=class extends Up{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return D(()=>{let i=(p,d)=>Dt([In([68],p,"float32"),In([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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t=[],{extractDenseBlock3Params:n}=zf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return xn(e,t),{params:a,paramMappings:t}}function _C(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock3Params:r}=Of(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Tw=class extends tn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=ka(a,[122.782,117.001,104.298]).div(he(255)),i=Mf(s,n.dense0,!0);return i=Mf(i,n.dense1),i=Mf(i,n.dense2),i=Jn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ns=class extends tn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(512,!1),"float32"),r=ge(L(a,he(.007843137718737125)),he(1)),s=LC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=WC(s.out,s.conv11,n.prediction_layer);return BC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await yt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await yt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map($=>$*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map($=>$*f);return new bt(u[v],new ro(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return OC(t)}extractParams(t){return PC(t)}};function Ew(e){let t=new Ns;return t.extractWeights(e),t}function VC(e){return Ew(e)}var Aw=class extends Ns{};var UC=.4,GC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],HC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],jC=[117.001,114.697,97.404],qC="tiny_yolov2_model",KC="tiny_yolov2_separable_conv_model";var Hf=e=>typeof e=="number";function jf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Hf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Hf(t.x)&&Hf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Hf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,he(.10000000149011612));return Z(Ye(ge(e,t)),t)})}function Rr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=ge(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Pr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function Dse(e,t){let n=Mu(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Ru(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function XC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Dse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Mse(e,t){let n=Hn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function YC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Mse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ua=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Fw=class extends tn{constructor(t){super("TinyYolov2");jf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Rr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Rr(a,n.conv6),a=Rr(a,n.conv7),uo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(uo(t,n.conv0,"valid",!1)):Pr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Pr(a,n.conv6):a,a=n.conv7?Pr(a,n.conv7):a,uo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?ka(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ua(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return If(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Dr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return YC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Fw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return XC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?_a(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new ao(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Lu=Fw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var fo=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:UC,classes:["face"],...t?{anchors:HC,meanRgb:jC}:{anchors:GC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?KC:qC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function JC(e,t=!0){let n=new fo(t);return n.extractWeights(e),n}var Qp=class extends Ua{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function go(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Is(l)?r(l):l.detection),i=a||(t instanceof z?await lo(t,s):await oo(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function zu(e,t,n,a,r){return go([e],t,async s=>n(s[0]),a,r)}var ZC=.4,QC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],e_=[117.001,114.697,97.404];var yo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:ZC,classes:["face"],anchors:QC,meanRgb:e_,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Ns,tinyFaceDetector:new yo,tinyYolov2:new fo,faceLandmark68Net:new po,faceLandmark68TinyNet:new Kp,faceRecognitionNet:new ho,faceExpressionNet:new Gp,ageGenderNet:new jp},$w=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),t_=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),n_=(e,t)=>et.tinyYolov2.locateFaces(e,t),Dw=e=>et.faceLandmark68Net.detectLandmarks(e),a_=e=>et.faceLandmark68TinyNet.detectLandmarks(e),r_=e=>et.faceRecognitionNet.computeFaceDescriptor(e),s_=e=>et.faceExpressionNet.predictExpressions(e),i_=e=>et.ageGenderNet.predictAgeAndGender(e),Mw=e=>et.ssdMobilenetv1.load(e),o_=e=>et.tinyFaceDetector.load(e),l_=e=>et.tinyYolov2.load(e),u_=e=>et.faceLandmark68Net.load(e),c_=e=>et.faceLandmark68TinyNet.load(e),p_=e=>et.faceRecognitionNet.load(e),d_=e=>et.faceExpressionNet.load(e),h_=e=>et.ageGenderNet.load(e),m_=Mw,f_=$w,g_=Dw;var Rw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Rw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Hp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Rw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Hp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},vo=class extends Vu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},wo=class extends Uu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var Pw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Pw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Jp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Jp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},bo=class extends Bu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},xo=class extends Wu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var ed=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Or=class extends ed{async run(){let t=await this.parentTask;return(await go(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Yp(t[r],a))}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}},Lr=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Yp(t,n)}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}};var td=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},nd=class extends td{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await lo(this.input,n):await oo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof z&&s.dispose()),t.map((s,i)=>co(s,r[i]))}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},ad=class extends td{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await lo(this.input,[n]):await oo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),co(t,r)}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var rd=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Gu=class extends rd{async run(){let{input:t,options:n}=this,a=n instanceof Qp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof sa?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Ua?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new nd(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},sd=class extends rd{async run(){let t=await new Gu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new ad(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function y_(e,t=new sa){return new sd(e,t)}function id(e,t=new sa){return new Gu(e,t)}async function Ow(e,t){return id(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function b_(e,t={}){return id(e,new Ua(t)).withFaceLandmarks().withFaceDescriptors()}var x_=Ow;function qf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var od=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof ur)return i;if(i instanceof Float32Array)return new ur(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new ur(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>qf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>ur.fromJSON(a));return new od(n,t.distanceThreshold)}};function v_(e){let t=new yo;return t.extractWeights(e),t}function Lw(e,t){let{width:n,height:a}=new un(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Lw(r,{width:n,height:a}));if(Is(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return co(bs(e,r),s)}return Wa(e)?bs(e,e.detection.forSize(n,a)):e instanceof Gn||e instanceof bt?e.forSize(n,a):e}var Pse=typeof process!="undefined",Ose=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",w_={faceapi:wC,node:Pse,browser:Ose};return Rse;})(); /** * @license * Copyright 2017 Google LLC. 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Mo=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Do(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Eo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Mo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Do(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Eo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Jr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Jr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return 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lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:lo}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:lo,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{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 ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],sn=[117.001,114.697,97.404];var Fe=class extends ge{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 P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},cn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),mn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),pn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=pn,Ta=cn,Pa=mn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Xt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await 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this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await 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u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function Lo(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function to(o,t,e=!0){let r=e?I.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,Lo(o,t.expansion_conv,[2,2])),r}function Gn(o,t){let e=$(I.relu(o),t.separable_conv0,[1,1]);return e=$(I.relu(e),t.separable_conv1,[1,1]),e=$(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var eo=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(Lo(a,e.entry_flow.conv_in,[2,2]));return 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vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await 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d=p.score;for(let u=m.length-1;u>=0;--u){let l=ea(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function ra(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function oa(o,t){let{sizes:e,centers:r}=ra(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Vo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=oa(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var $e=b(g());var Oe=b(g());function Vt(o,t){return Oe.tidy(()=>{let e=o.shape[0],r=Oe.reshape(Gt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Oe.reshape(Gt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Uo(o,t,e){return $e.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Vt(t,e.box_predictor_0),u=Vt(o,e.box_predictor_1),l=Vt(n,e.box_predictor_2),v=Vt(s,e.box_predictor_3),_=Vt(c,e.box_predictor_4),h=Vt(p,e.box_predictor_5),y=$e.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,_.boxPredictionEncoding,h.boxPredictionEncoding],1),T=$e.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,_.classPrediction,h.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var Z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ut=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=Go(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Uo(a.out,a.conv11,e.prediction_layer);return Vo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*_);return new M(p[F],new re(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Yo(t)}extractParams(t){return Ho(t)}};function Xo(o){let t=new Ut;return t.extractWeights(o),t}function na(o){return Xo(o)}var Jo=class extends Ut{};var qo=.4,Zo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Ko=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Qo=[117.001,114.697,97.404],tn="tiny_yolov2_model",en="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(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(!br(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=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function rn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=aa(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,_,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,l,"conv1"),et=m(l,v,"conv2"),it=m(v,_,"conv3"),X=m(_,h,"conv4"),Pt=m(h,y,"conv5"),_t=T?m(y,T,"conv6"):void 0,wt=F?m(T,F,"conv7"):void 0,Qt=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:Qt}}else{let[d,u,l,v,_,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,l,"conv1"),et=c(l,v,"conv2"),it=c(v,_,"conv3"),X=c(_,h,"conv4"),Pt=c(h,y,"conv5"),_t=c(y,T,"conv6"),wt=c(T,F,"conv7"),Qt=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:Qt}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function sa(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:lo}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:lo,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{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 ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],sn=[117.001,114.697,97.404];var Fe=class extends ge{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 P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},cn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),mn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),pn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=pn,Ta=cn,Pa=mn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Xt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await 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u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function Lo(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function to(o,t,e=!0){let r=e?I.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,Lo(o,t.expansion_conv,[2,2])),r}function Gn(o,t){let e=$(I.relu(o),t.separable_conv0,[1,1]);return e=$(I.relu(e),t.separable_conv1,[1,1]),e=$(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var eo=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(Lo(a,e.entry_flow.conv_in,[2,2]));return s=to(s,e.entry_flow.reduction_block_0,!1),s=to(s,e.entry_flow.reduction_block_1),ct(this._numMainBlocks,0,1).forEach(i=>{s=Gn(s,e.middle_flow[`main_block_${i}`])}),s=to(s,e.exit_flow.reduction_block),s=I.relu($(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Io(t,this._numMainBlocks)}extractParams(t){return No(t,this._numMainBlocks)}};function ko(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function So(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return ko(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),So(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{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 H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(H.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return H.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 E(t))}async detectLandmarks(t){let e=await E(t),r=H.tidy(()=>H.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>ze(d)),m=i.filter((p,d)=>!ze(d));return new oe(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ut=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=Go(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Uo(a.out,a.conv11,e.prediction_layer);return Vo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*_);return new M(p[F],new re(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Yo(t)}extractParams(t){return Ho(t)}};function Xo(o){let t=new Ut;return t.extractWeights(o),t}function na(o){return Xo(o)}var Jo=class extends Ut{};var qo=.4,Zo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Ko=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Qo=[117.001,114.697,97.404],tn="tiny_yolov2_model",en="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(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(!br(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=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function rn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=aa(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,_,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,l,"conv1"),et=m(l,v,"conv2"),it=m(v,_,"conv3"),X=m(_,h,"conv4"),Pt=m(h,y,"conv5"),_t=T?m(y,T,"conv6"):void 0,wt=F?m(T,F,"conv7"):void 0,Qt=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:Qt}}else{let[d,u,l,v,_,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,l,"conv1"),et=c(l,v,"conv2"),it=c(v,_,"conv3"),X=c(_,h,"conv4"),Pt=c(h,y,"conv5"),_t=c(y,T,"conv6"),wt=c(T,F,"conv7"),Qt=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:Qt}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function sa(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:lo}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:lo,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{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 ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],sn=[117.001,114.697,97.404];var Fe=class extends ge{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 P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},cn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),mn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),pn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=pn,Ta=cn,Pa=mn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Xt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>$t({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new He(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?$t({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function _a(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new He(o,t)}async function dn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function 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Mo=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Do(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Eo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Mo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return 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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=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(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 W(o,e),{params:s,paramMappings:e}}var lt=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 io=class extends S{constructor(t){super("TinyYolov2");ao(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=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:lo}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:lo,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{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 ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],sn=[117.001,114.697,97.404];var Fe=class extends ge{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 P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},cn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),mn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),pn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=pn,Ta=cn,Pa=mn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Xt(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 hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>$t({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new He(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?$t({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function _a(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new He(o,t)}async function dn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=dn;function uo(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 Dr=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 xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(o){let t=new Fe;return t.extractWeights(o),t}function un(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>un(n,{width:e,height:r}));if(zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($t(o,n),a)}return pt(o)?$t(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ia={faceapi:Co,node:Ca,browser:Na}; //# sourceMappingURL=face-api.node.js.map